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Record W4407794681 · doi:10.2196/64796

Assessment of Community Stakeholders’ and Health Educators and Professionals’ Needs for the Continuous Enhancement of Sexual and Reproductive Health and Rights in Mali (Project CLEFS): Protocol for a Convergent Mixed Methods Study

2025· article· en· W4407794681 on OpenAlexaffvenue
Sabina Abou Malham, Doufain Traoré, Fatoumata Dicko, Gabriel Blouin-Genest, Jennyfer Boudreau, Drissa Mansa Sidibé, Souleymane Sidibé, Issa Souleymane Goïta, Aminata Sangaré, Mohamed Togo, D Diarra, Michèle Rietmann, Mahamane Maiga, Suzie Boulanger, Ann Isabelle Grégoire, David-Martin Milot, Djamal Berbiche, Sarah Stecko

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsHôpital Charles-Le MoyneCegep de Saint JeromeUniversité de Sherbrooke
Fundersnot available
KeywordsSexual and reproductive health and rightsProtocol (science)Reproductive healthHealth professionalsMedical educationMedicinePsychologyNursingPublic relationsReproductive rightsPolitical scienceAlternative medicineHealth careEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: In Mali, a lack of qualified human resources in primary health care and sexual and reproductive health and rights (SRHR) is one of the greatest barriers to the population's access to standard health services. Frontline professional training must be strengthened to respond to the needs of the population, particularly those of women and girls. Training must be conducted using an interdisciplinary and adapted approach to promote gender equality. OBJECTIVE: This study aims to identify the SRHR training needs among the community, educational actors, and primary health care providers. METHODS: A concurrent mixed methods design was adopted, using 2 methods. A quantitative method, through a cross-sectional analytical survey, will be conducted at the community level with university community health centers (CSCom-U) users and adolescents in CSCom-U health areas, as well as at the health education institution and community health centers levels with teachers, students, and interdisciplinary professional groups within the CSCom-U and district hospital maternity. Descriptive and inferential analyses will be conducted to process quantitative data. This research is at the stage of data analysis and interpretation. A qualitative method, based on 3 sources of data (focus groups, individual semistructured interviews, and document analysis), which involved the same targets as the quantitative component, with additional community actors such as Community Health Associations (Associations de santé communautaire) and Women's Service User Communities. A thematic analysis of the qualitative data using a mixed deductive and inductive method will be performed. RESULTS: : Field data collection took place from March to April 2022. Quantitative data from 3153 participants are being analyzed using SPSS. Qualitative data from 11 interviews and 27 focus groups were processed with Qualitative Data Analysis Miner. Data analysis is still ongoing. CONCLUSIONS: This study will provide a better understanding of adolescents and SRHR user's service needs in terms of health services availability and quality and SRHR knowledge, issues related to student training quality, the level of adequacy between the training offered and the actual needs of the service recipients, and the level of preparation and ability of teachers to provide quality teaching taking gender equity into account. The recommendations drawn from this assessment will propose concrete actions to improve women and girls' health services provided by professionals, and to better adapt the future health professionals' profiles to the needs of communities, particularly those of women and girls. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64796.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.070
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.070
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0700.029
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.003
Science and technology studies0.0050.003
Scholarly communication0.0030.002
Open science0.0040.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0290.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.447
GPT teacher head0.666
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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