Factors Influencing Primary Care Access for Common Mental Health Conditions Among Adults in West Africa: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Mental health conditions are expressed in various ways in different people, and access to health care for these conditions is affected by individual factors, health care provider factors, and contextual factors. These factors may be enablers or barriers to accessing primary care for mental health conditions. Studies have reported a gap in treatment for mental health conditions in many countries in West Africa due to barriers along the access pathway. However, to the best of our knowledge, there is yet to be a review of the factors influencing access to primary care for common mental health conditions among adults in West Africa. OBJECTIVE: Our scoping review will explore the factors influencing access to primary care for common mental health conditions among adults aged 18 years and older in West Africa from 2002 to 2024. METHODS: Our review will follow the approach to scoping reviews developed by Arksey and O'Malley in 2005. This approach has five stages: (1) identifying the research question; (2) identifying relevant studies; (3) selecting studies; (4) charting the data; and (5) collating, summarizing, and reporting the results. We will search electronic databases (PubMed, Embase, PsycINFO, Cairn.info, and Google Scholar), source gray literature from relevant websites (the World Health Organization and country-specific websites), and manually explore reference lists of relevant studies to identify eligible records. Pairs of independent authors (NYA-B, RNBA, VR, or DS) will screen the titles, abstracts, and full texts of studies based on predefined eligibility criteria. We will use a data extraction tool adopted from the JBI Manual for Evidence Synthesis to chart the data. Deductive, thematic analysis will be used to categorize factors influencing access to mental health care under predetermined themes. New themes derived from the literature will also be charted. RESULTS: Database searches were conducted between February 1, 2024, and February 12, 2024. As of July 2024, the review report is being drafted, and it will be disseminated through publication in a peer-reviewed journal. CONCLUSIONS: The results of the review will inform decision-making on policies, programs, and their implementation in West Africa to improve primary care access for mental health care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58890.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.125 | 0.108 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.071 | 0.013 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".