Community-Based Primary Healthcare Training for Physiotherapy Undergraduates: Perceptions of Physiotherapy Academics
Bibliographic record
Abstract
Community-based primary healthcare training for health science students is based on the tenets of primary healthcare. The approach seeks to provide clinical education and training for health science students in previously disenfranchised communities. Some South African universities train their physiotherapy students through a community-based primary healthcare approach to undergraduate training. However, there is currently a lack of an integrated model guiding clinical education for physiotherapy clinical education in the country. The aim of this paper was to explore the perceptions of physiotherapy academics about a novel, community-based primary healthcare approach to clinical education for students at a university in South Africa. This study sought to inform the roll-out of an evidence-based model for physiotherapy education. A qualitative explorative approach, using semi-structured interviews with physiotherapy academics at the institution, was used to explore their perceptions of the community-based primary healthcare training platform. Data was analysed using conventional content analysis and was classified into themes and sub-themes. Four overarching themes emerged, namely: curriculum review, constraints to decentralised learning, benefits of community-based clinical education and recommendations for the learning platform. Participating academics believed that community-based primary healthcare training is an approach that influences students to be socially responsive, while providing access to healthcare services, such as rehabilitation, in resource-poor communities in South Africa.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".