The Barriers to Interprofessional Collaborative Practice: Perspectives from Australian Physiotherapy Private Practitioners
Bibliographic record
Abstract
Background: Despite the growing presence of physiotherapy private practitioners within Australia’s healthcare workforce, little is known about their perspectives of interprofessional collaborative practice (IPCP). This study aims to explore the barriers to IPCP from the perspective of Australian physiotherapy private practitioners. Methods: Semi-structured interviews were conducted with 28 physiotherapists and 64 hours of observation was completed in 10 private practice sites in Queensland, Australia. Interview and observation data were pooled and analyzed using reflexive thematic analysis. Findings: Data analysis produced five themes that characterized physiotherapists’ perspectives of IPCP: a) competition for clientele, b) personal attitudes and beliefs, c) time constraints and work schedules, d) geographic location, and e) rules of funding schemes. Conclusion: The findings from this study suggest that implementing IPCP in the Australian physiotherapy private practice setting presents several challenges. Financial concerns, such as physiotherapy private practitioners’ perceived need to compete for clientele, were significant barriers to IPCP. The introduction of financial incentives and adoption of alternative payment models may be necessary to provide physiotherapy private practitioners with a clear motivation to engage in IPCP. The need for more formal opportunities to bring health practitioners from diverse professional backgrounds together to gain new insights and knowledge of other professions’ expertise and challenge their own assumptions was also highlighted.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".