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Validation and Predictive Utility of a Person-Centered Quality of Contraceptive Counseling (QCC-10) Scale in Sub-Saharan Africa: A Multicountry Study of Family Planning Clients and a New Indicator for Measuring High-Quality, Rights-Based Care
research-article
Author(s):
Celia Karp ,
Funmilola M. OlaOlorun ,
Georges Guiella ,
Peter Gichangi ,
Yoonjoung Choi ,
Philip Anglewicz ,
Kelsey Holt
Publication date (Electronic):
14 February 2023
Journal:
Studies in family planning
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Abstract
The lack of validated, cross-cultural measures for examining quality of contraceptive counseling compromises progress toward improved services. We tested the validity and reliability of the 10-item Quality of Contraceptive Counseling scale (QCC-10) and its association with continued protection from unintended pregnancy and person-centered outcomes using longitudinal data from women aged 15–49 in Burkina Faso, Kenya, andNigeria. Psychometric analysis showed moderate-to-strong reliability (alphas: 0.73–0.91) and high convergent validity with greatest service satisfaction. At follow-up, QCC-10 scores were not associated with continued pregnancy protection but were linked to contraceptive informational needs being met among Burkinabe and Kenyan women; the reverse was true in Kano. Higher QCC-10 scores were also associated with care-seeking among Kenyan women experiencing side effects. The QCC-10 is a validated scale for assessing quality of contraceptive counseling across diverse contexts. Future work is needed to improve understanding of how the QCC-10 relates to person-centered measures of reproductive health.
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Most cited references42
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Women's preferences for contraceptive counseling and decision making.
Christine Dehlendorf, Kevin Grumbach, Julie Levy … (2013)
Little is known about what women value in their interactions with family planning providers and in decision making about contraception. We conducted semistructured interviews with 42 black, white and Latina patients. Transcripts were coded using modified grounded theory. While women wanted control over the ultimate selection of a method, most also wanted their provider to participate in the decision-making process in a way that emphasized the women's values and preferences. Women desired an intimate, friend-like relationship with their providers and also wanted to receive comprehensive information about options, particularly about side effects. More black and Spanish-speaking Latinas, as compared to whites and English-speaking Latinas, felt that providers should only share their opinion if it is elicited by a patient or if they make their rationale clear to the patient. While, in the absence of medical contraindications, decision making about contraception has often been conceptualized as a woman's autonomous decision, our data indicate that providers of contraceptive counseling can participate in the decision-making process within limits. Differences in preferences seen by race/ethnicity illustrate one example of the importance of individualizing counseling to match women's preferences. Copyright © 2013 Elsevier Inc. All rights reserved.
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PMA2020: Rapid Turn‐Around Survey Data to Monitor Family Planning Service and Practice in Ten Countries
Linnea Zimmerman, Hannah Olson, Amy Tsui … (2017)
CONTENTS Research Questions for Original Data Collection In 2012, the London Summit on Family Planning adopted the ambitious goal of increasing access to contraception for 120 million additional women and girls in the world's poorest countries by 2020. Family Planning 2020 (FP2020)1 was established as a coordinating body to monitor progress. In order to monitor country progress and to change course in the event of stagnating or declining use, data were needed more frequently and more quickly than data provided by typical surveys. Performance Monitoring and Accountability 2020 (PMA2020) was created to provide rapid and frequent estimates of modern contraceptive use in FP2020 priority countries. Currently operational in ten countries (Burkina Faso, DRC, Ethiopia, Ghana, India, Indonesia, Kenya, Niger, Nigeria, and Uganda), PMA2020 conducts surveys every six months to one year, providing FP2020, governments, and other stakeholders frequent information on contraceptive use, demand, and supply that can inform policies and programs and identify areas for improvement. PMA2020 recruits women from within or near selected enumerations areas (EAs) and trains them in collecting household and facility‐level data using smartphones and submitting the data to a cloud server. These resident enumerators (REs) are then deployed to collect data on a repeated basis—each round within a six‐week period—with refresher training between each round. Household data include information on household members, as well as assets, livestock ownership, housing construction, and water, sanitation, and hygiene (WASH) conditions. Women aged 15–49 who are either usual members of the household or who slept in the household the night before the interview are eligible for the female interview. The female survey gathers information on sociodemographic characteristics, such as education and marital status, as well as measures of fertility and contraceptive use, including the dates of women's first and two most recent births, age at first sex, age at first marriage, and age and parity at first contraceptive use. Family planning measures include current use of contraception and contraceptive use within 12 months preceding the interview among current non‐users, by method previously used. The data also include reasons for not using or stopping a method of contraception, intention to use contraception in the future among non‐users, autonomy and influences related to contraceptive decision‐making, and the “method information index”—whether she was told about any methods other than the one she chose, whether she received counseling on side effects, and whether she was told what to do if she experienced side effects. Several additional quality and choice indicators can be calculated. Constructed variables in the dataset include wealth quintiles/tertiles, unmet need for family planning, and current use of a modern contraceptive method. While family planning is the focus of the survey, a small number of water and sanitation questions have been added to the household, female, and service delivery point (SDP) questionnaires. The WASH questions that are asked in the household and female surveys are round‐specific and cover topics such as distance to a water source, handling of child waste, diarrhea prevalence among children under 5 years of age, and menstrual hygiene management. The range of topics covered to date demonstrates the flexibility of the PMA platform: once the data collection platform is established, data can be collected to support other areas of health intervention. Such expansions are underway in selected countries with respect to primary health care, maternal and newborn health, schistosomiasis, and nutrition. Data for SDPs are collected using a health facility questionnaire that is fielded concurrently with the household/female data collection. The SDP dataset includes measures of contraceptive availability, stock‐outs, numbers of clients served, outreach through mobile services and community health workers, and integration of family planning with other health services such as HIV, maternal health, and post‐abortion care. The SDP dataset also includes measures of service quality, such as availability of supplies for both insertion and removal of intrauterine devices (IUDs) and implants, storage conditions for contraceptive commodities, and availability of adequate hand‐washing stations for providers. The SDP dataset includes variables that identify the enumeration areas that each SDP serves. These enumeration areas are the same EAs as in the household dataset, which allows for linking at the community level between households and the health service environment. Sample Selection and Size, including Response Rates and Loss to Follow‐Up The PMA2020 household and female survey uses a multi‐stage cluster sample design to draw a probability sample of households and eligible females. The indicator used to calculate the female survey sample size is the modern contraceptive prevalence rate (mCPR) among all women aged 15–49 with a maximum margin of error of ±3 percentage points at the national level and a maximum of ±5 percentage points for urban/rural strata. In some countries sample sizes are sufficient to produce sub‐national estimates, generally at the next lowest administrative level. Country‐specific sampling descriptions specify the level at which estimates are representative. Table 1 summarizes the administrative levels at which the estimates are representative and the associated margins of error. Table 1 Level at which data are representative and margin of error by country Country Level at which data are representative Margin of error used for original sample size calculation Ghana National <2% Urban/Rural <3% DRC Kinshasa <2% Kongo Central <2% Ethiopia National <2% Urban/Rural <3% 5 regionsa 5% Uganda National 2% Urban/Rural <3% Kenya National <3% Urban/Rural 3% 9 countiesb 5% Burkina Faso National 2% Urban/Rural <5% Nigeria National <2% Urban/Rural <2% 7 statesc <2‐3% Niger National <2% Niamey 3% Urban/Rural <3% Indonesia National <2% Urban/Rural <3% South Sulawesi <3% Makassar district 5% India Rajasthan 2% Urban/Rural 3% a Addis, Amhara, Oromiya, Tigray, and SNNPR b Bungoma, Kericho, Kiambu, Kilifi, Kitui, Nairobi, Nandi, Nyamira, Siaya c Anambra, Kaduna, Kano, Lagos, Nasarawa, Rivers, Taraba John Wiley & Sons, Ltd. Each EA has approximately 200 households. At the EA level, the RE lists and maps all households and private health facilities. She is assigned a random selection of 33–44 households (depending on the country) and obtains the consent of household and female respondents for interviews. All data collection is approved by country‐specific IRBs. Private SDPs are included in the sample if they fall within the boundaries of the enumeration areas. Up to three randomly chosen private facilities are selected from each EA. Public health facilities are included in the sample if the selected EA falls into the catchment area of the SDP. Implementing partners obtain a list of public health facilities assigned to provide services to residents in the selected EAs; facilities at the lowest level (equivalent to a health post), secondary level (e.g., health center), and tertiary level (e.g., referral hospital) are selected into the sample. The SDP sample thus reflects the services available to a representative population, rather than being representative of all SDPs in the country. If a national frame of SDPs, both public and private, is available, the PMA SDP sample can be weighted to provide measures representative of facilities at the national level. Subsequent survey rounds are conducted in the same EAs, but with a new sample selection of households. SDPs tend to be the same facilities between survey rounds since the public‐sector facilities that serve a particular enumeration area are not likely to change. If there are more than three private facilities within an enumeration area, three are randomly selected in each round. At the fifth round of data collection, new enumeration areas are selected to limit respondent fatigue and possible interview effects in the community. New enumeration areas are generally geographically contiguous to the original EAs and share the same urban/rural designation. Table 2 shows the response rates for each survey and round by household and the complete female sample (both usual household members and visitors). Analyses conducted by PMA2020 include only regular household members. Table 3 shows the rates for the SDP data. Since PMA2020 data are cross‐sectional, there is no follow‐up and thus no loss to follow‐up. Table 2 Household and Female Response Rates Across PMA2020 Surveys Country and round Data collection period Households selected Households occupied Households interviewed Household response rate (%) Total eligible women Eligible women interviewed Eligible women response rate (%) Ghana Round 1 Oct‐Dec 2013 4072 3910 3536 90.4 4191 3708 88.9 Ghana Round 2 Jan‐May 2014 4148 3802 3419 89.9 4264 3974 93.5 Ghana Round 3 Oct‐Dec 2014 4164 4072 3927 96.4 4806 4621 96.6 Ghana Round 4 May‐Jun 2015 4186 4142 4053 97.9 5391 5234 97.5 Ghana Round 5 Aug‐Dec 2016 4182 4118 4062 98.6 3860 3746 97.0 DRC/Kinshasa Round 1 Nov 2013‐Jan 2014 * * 1777 * 2160 2132 98.7 DRC/Kinshasa Round 2 Aug‐Sep 2014 * * 1900 * 3017 2877 95.4 DRC/Kinshasa Round 3 May‐Jun 2015 1844 1828 1768 96.7 2841 2683 95.3 DRC/Kinshasa Round 4 Nov‐Dec 2015 1918 1843 1774 96.3 2869 2741 96.1 DRC/Kongo Central Round 4 Nov‐Dec 2015 1720 1688 1625 96.3 1653 1573 95.4 DRC/Kinshasa Round 5 Aug‐Sep 2016 1914 1895 1841 97.2 2733 2593 94.9 DRC/Kongo Central Round 5 Aug‐Sep 2016 1715 1641 1575 96.0 1756 1688 96.1 Ethiopia Round 1 Jan‐Mar 2014 6979 6919 6772 97.9 6688 6514 97.6 Ethiopia Round 2 Sep‐Nov 2014 6997 6927 6813 98.4 6888 6713 97.5 Ethiopia Round 3 Apr‐Jun 2015 7735 7703 7643 99.2 7708 7628 99.0 Ethiopia Round 4 Mar‐May 2016 7732 7695 7651 99.4 7642 7537 98.6 Uganda Round 1 Apr‐Jun 2014 4802 4576 4257 93.0 3987 3754 94.4 Uganda Round 2 Jan‐Feb 2015 4840 4429 4143 93.5 3859 3654 94.7 Uganda Round 3 Aug‐Oct 2015 4838 4671 4412 94.5 3889 3705 95.3 Uganda Round 4 Mar‐Apr 2016 4839 4433 4191 94.5 4044 3816 94.4 Kenya Round 1 May‐Jul 2014 5040 4859 4518 93.0 3987 3792 95.7 Kenya Round 2 Nov‐Dec 2014 5038 4803 4604 95.9 4470 4370 97.9 Kenya Round 3 May‐Jun 2015 5040 4958 4810 97.0 4506 4433 98.4 Kenya Round 4 Nov‐Dec 2015 5039 4928 4792 97.2 5025 4960 98.7 Kenya Round 5 Nov 2016–Jan 2017 6343 6239 6073 97.3 6050 5975 98.8 Burkina Faso Round 1 Nov 2015‐Jan 2016 1857 1810 1760 97.2 2220 2094 94.3 Burkina Faso Round 2 Apr‐Jun 2015 1855 1778 1733 97.5 2270 2150 94.7 Burkina Faso Round 3 Mar‐Apr 2016 2906 2864 2803 97.9 3493 3353 96.1 Burkina Faso Round 4 Nov 2016‐Jan 2017 2904 2807 2751 98.0 3414 3245 95.1 Nigeria/Kaduna Round 1 Sep‐Oct 2014 2309 2287 2194 95.9 2637 2575 97.9 Nigeria/Lagos Round 1 Sep‐Oct 2014 1302 1233 974 79.0 864 771 89.3 Nigeria/Kaduna Round 2 Sep‐Oct 2015 2308 2288 2264 99.0 3006 2943 97.9 Nigeria/Lagos Round 2 Sep‐Oct 2015 2080 1982 1777 89.7 1617 1449 89.8 Nigeria National Round 3 May‐Jul 2016 10815 10436 10131 97.1 11463 11150 97.4 Niger/Niamey Round 1 Jul‐Aug 2015 1155 1143 1129 98.8 1408 1352 96.0 Niger National Round 2 Feb‐Apr 2016 2894 2833 2787 98.4 3193 3042 95.3 Niger/Niamey Round 3 Nov‐Dec 2016 1146 1127 1099 97.5 1443 1410 97.7 Indonesia Round 1 Jun‐Aug 2015 12963 12537 11726 93.5 11618 10566 91.0 India/Rajasthan Round 1 May‐Sep 2016 5116 5002 4870 97.4 5741 5456 95.0 *In DRC Rounds 1 and 2, only household forms that were completed were uploaded and saved. It is thus not possible to calculate % of households occupied or non‐response rates for these two rounds. John Wiley & Sons, Ltd. Table 3 SDP Response Rates Across PMA2020 Surveys Country and round SDPs identified SDPs completed SDP response rate (%) Ghana Round 1 143 138 96.5 Ghana Round 2 132 124 93.9 Ghana Round 3 241 231 95.9 Ghana Round 4 239 233 97.5 Ghana Round 5 176 157 89.2 DRC/Kinshasa Round 2* 257 248 96.5 DRC/Kinshasa Round 3 255 248 97.3 DRC/Kinshasa Round 4 239 228 95.4 DRC/Kongo Central Round 4 122 120 98.4 DRC/Kinshasa Round 5 185 173 93.5 DRC/Kongo Central Round 5 105 102 97.1 Ethiopia Round 1 397 389 98.0 Ethiopia Round 2 407 398 97.8 Ethiopia Round 3 453 445 98.2 Ethiopia Round 4 461 456 98.9 Uganda Round 2* 373 362 97.1 Uganda Round 3 379 363 95.8 Uganda Round 4 384 350 91.1 Kenya Round 1 277 264 95.3 Kenya Round 2 354 324 91.5 Kenya Round 3 359 348 96.9 Kenya Round 4 358 338 94.4 Kenya Round 5 429 410 95.6 Burkina Faso Round 1 107 106 99.1 Burkina Faso Round 2 107 100 93.5 Burkina Faso Round 3 134 132 98.5 Burkina Faso Round 4 135 131 97.0 Nigeria/Kaduna Round 1 137 135 98.5 Nigeria/Lagos Round 1 94 87 92.6 Nigeria/Kaduna Round 2 152 148 97.4 Nigeria/Lagos Round 2 132 123 93.2 Nigeria National Round 3 694 667 96.1 Niger/Niamey Round 1 33 31 93.9 Niger National Round 2 138 132 95.7 Niger/Niamey Round 3 30 27 90.0 Indonesia Round 1 940 885 94.1 India/Rajasthan Round 1 308 294 95.6 *No Round 1 SDP survey was conducted in this country/round John Wiley & Sons, Ltd. Data Quality PMA2020 employs automated checks to monitor and improve data quality. Progress and error reports are run daily by in‐country data managers. These reports track progress in the number of interviews conducted and transmitted to the server, monitor response rates, and identify potential data quality issues, including flagging questions and interviewers with high rates of non‐response, flagging missing or incomplete forms, and using GPS locations to track the geographic distribution of interviews. Additionally, PMA2020 has developed tools called “PMA Analytics” that record how long each question appears on the screen before moving forward. This is a proxy for the amount of time it takes to ask and record each response, which is useful for identifying any falsified or questionable data. Estimates of modern contraceptive use, the key indicator used to determine sample size, have been broadly consistent across countries and rounds. Figure 1 shows mCPR estimates for married women in Ethiopia and Uganda generated by the FPET models used by Track20.2 The PMA estimates are consistent with trends shown by other estimates and indicate increases across rounds, with some variability. There is variability over time in all countries due to sampling error, but overall the estimates for mCPR show consistent increases. New EAs are selected in Round 5 in the event that family planning awareness at the community level has increased as a result of RE interviews. This addition allows further consistency comparisons. Figure 1 Trends in modern contraceptive prevalence rate among married women in Ethiopia and Uganda NOTE: Generated using FPET tool on April 10, 2017. Citation: New, JR and Alkema, L (2015). Family Planning Estimation Tool (FPET). Available at http://fpet.track20.org/ Despite robust data checks, reporting biases and measurement errors may occur. To provide additional information on its design and protocols, PMA is introducing a series of methodological reports, available through the website, that summarize and review data quality issues and the effect these may have on estimates. The first such report, “Response patterns on behavioral outcomes in relation to use of resident enumerators over multiple survey rounds,” reviews the effect of using resident enumerators on response patterns. Future reports will explore such topics as the effect of date misreporting and results from PMA Analytics. Data Formats PMA2020 data are available in a variety of formats, including pre‐calculated indicators, interactive tables, and individual and household‐level microdata. Pre‐calculated indicators presented in Snapshot of Indicators (SOI) tables, charts (DataLab), and published briefs are available on the PMA2020 website (www.pma2020.org). Estimates for both DataLab and SOI tables are generated using standard Stata do‐files and cross‐checked between DataLab and SOI tables for consistency prior to being published. After each round of data collection, priority FP indicators and figures are made available in the “Key Family Planning Indicator Briefs,” and detailed analyses of priority indicators disaggregated by standard demographic characteristics are available through the SOI tables and PMA2020 DataLab. Descriptions of the original sample selection and any round‐specific updates to the sampling, round‐specific questionnaires, response rates, and sample error estimates are published on the website, accompanying each SOI table. The PMA website also contains additional memos describing the sampling procedure and assumptions used by PMA, general guidance on the construction of sample weights, and description and definition of key indicators. Sample errors are provided for key indicators in additional tables. Microdata are available in csv, xls, or dta format. While PMA2020 data are cleaned during data collection, very little is done to change the content of the data; that is, missing values are not imputed, and extreme values are not corrected. Content is changed only when a skip pattern necessitates a correction in the data, such as making the date of first birth and the date of most recent birth the same value for women who have had only one child. Otherwise, non‐response and extreme values are left to be corrected at the discretion of the analyst. All observations are provided, including interviews that were not completed, to allow users to reconstruct response rates. All identifying information, including names and sub‐regional geographic identifiers, are deleted prior to release to protect the anonymity of PMA2020 respondents. How and When Data Were Collected Since its inception in April 2013, the PMA2020 project, in partnership with country research organizations, has completed 39 nationally or sub‐nationally representative household and health facility surveys in ten low‐ or middle‐income countries. Dates of data collection are provided in Table 2. All interviews with household, female, and SDP respondents are conducted face‐to‐face, and responses are entered into an Android smartphone using Open Data Kit (ODK) software. Following the interview, data are submitted to a secure cloud server, where they are instantly aggregated. Data are monitored daily by in‐country data management and quality assurance teams, with technical assistance provided by the PMA2020 team at Johns Hopkins University (JHU). Fieldwork is generally completed within 4–6 weeks, with preliminary cross‐tabulations of the data prepared as charts and tables within another 4–6 weeks. The majority of questions included in the PMA2020 household and female questionnaires replicate wording from the Demographic and Health Surveys (DHS), and many of those used in the SDP survey replicate questions in the Service Provision Assessment (SPA). In terms of measurement reliability, most results from the two types of surveys should be directly comparable. The PMA2020 female and SDP survey questionnaires are designed to measure indicators that are essential to FP2020 and national family planning efforts. This constraint on content keeps the questionnaire focused and brief enough to be administered in a short period. Data Location and Access PMA2020 microdata are accessible on request through the project website (http://www.pma2020.org/request-access-to-datasets-new) upon approval by PMA2020's coordinating center at JHU in Baltimore. Datasets are made publicly available approximately six months after data collection is completed. To view publicly available datasets and to obtain online access to PMA2020 datasets, users must create an account and submit a brief description of the research question. Requests can be submitted in either English or French. While the dataset language (variables/value labels) is English, dataset user notes and the codebook are also available in French for DRC, Niger, and Burkina Faso. Users granted access will be linked to a website from which the relevant materials can be downloaded. If a dataset is updated, users who have received approval to download the data will be notified by email. Only one version of each dataset will be available through the website. All datasets are archived and specific datasets can be made available upon request and review. USE Estimates in the two‐page family planning briefs are preliminary based on having a minimum of 95 percent of expected interviews submitted; there may be slight variation in those estimates and estimates presented in the DataLab or SOI tables. Estimates provided in the DataLab and SOI tables are based on final datasets that are released publicly and should be consistent between the two sources. The SOI tables will indicate whether estimates are based on small sample sizes; DataLab does not do so. Particularly for SDP indicators, it is recommended that users consult both sources to check adequacy of sample sizes. Although user support for analysis of the microdata is limited, the dataset user notes provide a brief description of the variables that can help identify households, individual females, and SDPs. The notes detail country‐specific variables and other variables of interest, including constructed ones generated for analysis. The notes also include a brief sample description, details on the criteria PMA2020 employs for inclusion in the analytic sample, and explanations of any anomalies in the data. If a dataset has been updated, the user notes will list the variables that were changed and indicate the changes made. It is recommended that users review the dataset notes before analyzing the data. Given the constraints in using ODK software, both the month and year of a date must be entered. If a date is entirely unknown, it is entered as January 1, 2020. If the year is known but the month is not, the default month is set to January. It is recommended that analysts review the distribution of events by month. In‐country partner institutions reserve the right to limit access to selected variables for up to one year if the data collection is funded through partner relationships that require restricted access. It is suggested that publications based on PMA2020 data include the following citation: Performance Monitoring and Accountability 2020 (PMA2020) Project, [name of the relevant PMA2020 partner institution(s)]. [Survey year]. [Country]. Baltimore, MD: PMA2020, Bill & Melinda Gates Institute for Population and Reproductive Health, Johns Hopkins Bloomberg School of Public Health. The suggested citation is provided with the datasets. VALUE OF THE DATA The unique scientific value of PMA2020 survey data lies in the following features: PMA2020 provides nationally representative survey data on family planning indicators with rapid turnaround on an annual or more frequent basis. It collects information directly from facilities that provide family planning services to the sampled households. By combining both facility and household components of the PMA2020 platform, researchers can set up both the supply and demand sides for analysis of the association between family planning service delivery outputs and the population outcomes in a way that few other facility surveys can. This allows for the generation of unique insights and hypothesis‐testing. The selected geographic locations for PMA2020 surveys prioritize the FP2020 pledging countries to serve as a monitoring platform for ensuring that FP2020 goals and commitments are being met. In addition to providing comparable measures of core FP indicators, PMA2020 gathers information on emerging issues in FP and reproductive health that other large‐scale surveys do not capture. PMA has included questions in selected countries on implant removal, menstrual hygiene management, Sayana Press introduction, emergency contraceptive use, abortion, contraceptive acceptability, and program exposure. Enumeration areas and resident enumerators that are included in multiple rounds are masked with the same identifiers across rounds. It is thus possible to link interviews conducted in the same geographic area or conducted by the same interviewer over time, allowing for the investigation of longitudinal change and/or interviewer effects over time.
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Contraceptive Autonomy: Conceptions and Measurement of a Novel Family Planning Indicator.
Leigh Senderowicz (2020)
Since the 1994 International Conference on Population and Development, there has been increased attention to high-quality and rights-based family planning, but these concepts have been difficult to measure. Perhaps due to an intellectual history intertwined with population control, contemporary family planning programs and researchers often use (modern) method use as a primary marker of success, with indicators focusing narrowly on contraceptive use and fertility. This results in a fundamental misalignment between existing metrics and the stated family planning goals of promoting reproductive health and rights. This report describes the rationale for a novel family planning indicator called "contraceptive autonomy" and proposes a methodology for measuring this concept at the population level. Defining contraceptive autonomy as the factors necessary for a person to decide for themself what they want in relation to contraception and then to realize that decision, this indicator divides the contraceptive autonomy construct into subdomains of informed choice, full choice, and free choice. By acknowledging that autonomous nonuse is a positive outcome,aiming to maximize contraceptive autonomy rather than use could help shift incentives for family planning programs and reduce some common forms of contraceptive coercion, as our measurement approach is realigned with our focus on high-quality rights-based care.
Author and article information
Contributors
Celia Karp:
ORCID: http://orcid.org/0000-0002-4050-7602
Funmilola M. OlaOlorun:
ORCID: http://orcid.org/0000-0001-5544-2875
Georges Guiella:
ORCID: http://orcid.org/0000-0001-8525-340X
Peter Gichangi:
ORCID: http://orcid.org/0000-0001-9636-165X
Yoonjoung Choi:
ORCID: http://orcid.org/0000-0002-1116-0937
Philip Anglewicz:
ORCID: http://orcid.org/0000-0002-7967-3843
Kelsey Holt:
ORCID: http://orcid.org/0000-0001-9093-4322
Journal
Journal ID (nlm-journal-id): 7810364
Journal ID (pubmed-jr-id): 7621
Journal ID (nlm-ta): Stud Fam Plann
Journal ID (iso-abbrev): Stud Fam Plann
Title:
Studies in family planning
ISSN
(Print):
0039-3665
ISSN
(Electronic):
1728-4465
Publication date Nihms-submitted: 30
May
2024
Publication date
(Print):
March
2023
Publication date
(Electronic):
14
February
2023
Publication date PMC-release: 05
June
2024
Volume: 54
Issue: 1
Pages: 119-143
Affiliations
Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg
School of Public Health, Baltimore, MD, 21205, USA.
Institut Supérieur des Sciences de la Population (ISSP/University Joseph Ki-Zerbo),
Ouagadougou, Burkina Faso.
Author notes
Author information
Celia Karp
http://orcid.org/0000-0002-4050-7602
Funmilola M. OlaOlorun
http://orcid.org/0000-0001-5544-2875
Georges Guiella
http://orcid.org/0000-0001-8525-340X
Peter Gichangi
http://orcid.org/0000-0001-9636-165X
Yoonjoung Choi
http://orcid.org/0000-0002-1116-0937
Philip Anglewicz
http://orcid.org/0000-0002-7967-3843
Kelsey Holt
http://orcid.org/0000-0001-9093-4322
Article
Manuscript ID: GATESMANU1991485DOI: 10.1111/sifp.12229
PMC ID: 11152181
PubMed ID: 36787283
SO-VID: ca5f09bc-4c58-4223-8f0f-eb16f8ad5898
License:
This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
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