Expert opinion regarding the preparation of entry-level physiotherapists for primary healthcare practice, examined using Biggs 3P’s model of teaching learning
Bibliographic record
Abstract
INTRODUCTION: The scope of contemporary physiotherapy practice is a critical factor in determining the appropriate educational preparation for physiotherapists now and into the future. The world-wide shift from secondary to primary healthcare has, and is, continuing to result in new and different ways of working. It is crucial that curricular changes reflect these developments. In this study a qualitative approach using Biggs 3P's - Pressage, Process and Product model to discuss curriculum design. OBJECTIVE: The aim of the study was to explore the perspectives of both national and international physiotherapy educators/practitioners in primary healthcare, on the key elements required in physiotherapy education programmes to prepare future primary healthcare practitioners. METHODS: Snowball sampling was used to identify experts in education and/or primary healthcare practice. Semi-structured interviews were conducted using an interview guide based on the Biggs 3P's model. PARTICIPANTS: Twelve participants were recruited from Ireland (n = 2), the UK (n = 4), Canada (n = 3), New Zealand (n = 2) and Australia (n = 1) using snowball sampling. RESULTS: Interviews were analysed using thematic analysis. Themes identified included; understanding the philosophy of physiotherapy practice, cultural competence, inter-disciplinary team working and communication skills. Contextual factors and teaching and learning strategies were discussed. CONCLUSIONS: There is an urgent need for physiotherapy education programmes to adopt the concept of primary healthcare as the basis for the physiotherapy curriculum and illuminate key components for consideration.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".