Qualitative research in physiotherapy: A systematic mapping review of 20 years literature from sub-Saharan Africa
Bibliographic record
Abstract
STUDY AIM: To summarize the current state and quality of qualitative research conducted by physiotherapists in sub-Saharan Africa (SSA). METHODS: We systematically searched multiple databases from 2000 to December 2020 and included peer-reviewed qualitative studies conducted by physiotherapists in SSA countries. Two reviewers independently screened citations, extracted data, and assessed the quality of the included studies using the 45-items checklist by Lundgren, and colleagues. Conventional content analysis was employed to create physiotherapy subject areas from the included studies. RESULTS: We included 114 studies, a majority of 84 (74%) conducted in South Africa. Included studies were categorized into five subject areas: sports (n = 2), disability (n = 16), professional practice (n = 24), education and training (n = 36), and care provision (n = 36). We rated 74 (65%), 29 (25%), and 11 (10%) of the included research as low reporting quality, moderate- and high reporting quality, respectively. There was a significant lack of reporting on researchers' team characteristics, reflexivity, and member checking. CONCLUSION: We conclude that the reporting of published qualitative studies in SSA shows variable quality, albeit mostly low, focused mainly on care provision, education, and training. Physiotherapy-researchers are encouraged to report reflexive practice and member checking when conducting qualitative research.
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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 | 0.071 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.053 | 0.046 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".