Health research capacity building of health workers in fragile and conflict-affected settings: a scoping review of challenges, strengths, and recommendations
Bibliographic record
Abstract
BACKGROUND: Fragile and conflict-affected settings (FCAS) have a strong need to improve the capacity of local health workers to conduct health research in order to improve health policy and health outcomes. Health research capacity building (HRCB) programmes are ideal to equip health workers with the needed skills and knowledge to design and lead health-related research initiatives. The study aimed to review the characteristics of HRCB studies in FCASs in order to identify their strengths and weaknesses, and to recommend future directions for the field. METHODS: We conducted a scoping review and searched four databases for peer-reviewed articles that reported an HRCB initiative targeting health workers in a FCAS and published after 2010. Commentaries and editorials, cross-sectional studies, presentations, and interventions that did not have a capacity building component were excluded. Data on bibliographies of the studies and HRCB interventions and their outcomes were extracted. A descriptive approach was used to report the data, and a thematic approach was used to analyse the qualitative data. RESULTS: Out of 8822 articles, a total of 20 were included based on the eligibility criteria. Most of the initiatives centred around topics of health research methodology (70%), targeted an individual-level capacity building angle (95%), and were delivered in university or hospital settings (75%). Ten themes were identified and grouped into three categories. Significant challenges revolved around the lack of local research culture, shortages in logistic capability, interpersonal difficulties, and limited assessment and evaluation of HRCB programmes. Strengths of HRCB interventions included being locally driven, incorporating interactive pedagogies, and promoting multidisciplinary and holistic training. Common recommendations covered by the studies included opportunities to improve the content, logistics, and overarching structural components of HRCB initiatives. CONCLUSION: Our findings have important implications on health research policy and related capacity building efforts. Importantly, FCASs should prioritize (1) funding HRCB efforts, (2) strengthening equitable international, regional, and national partnerships, (3) delivering locally led HRCB programmes, (4) ensuring long-term evaluations and implementing programmes at multiple levels of the healthcare system, and (5) adopting engaging and interactive approaches.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.153 | 0.338 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.051 | 0.043 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".