Role of Australian primary healthcare organisations (PHCOs) in primary healthcare (PHC) workforce planning: lessons from abroad
Bibliographic record
Abstract
OBJECTIVE: To review international experience in order to inform Australian PHC workforce policy on the role of primary healthcare organisations (PHCOs/Medicare Locals) in PHC workforce planning. METHOD: A NZ and UK study tour was conducted by the lead author, involving 29 key informant interviews with regard to PHCOs roles and the effect on PHC workforce planning. Interviews were audio-taped with consent, transcribed and analysed thematically. RESULTS: Emerging themes included: workforce planning is a complex, dynamic, iterative process and key criteria exist for doing workforce planning well; PHCOs lacked a PHC workforce policy framework to do workforce planning; PHCOs lacked authority, power and appropriate funding to do workforce planning; there is a need to align workforce planning with service planning; and a PHC Workforce Planning and Development Benchmarking Database is essential for local planning and evaluating workforce reforms. CONCLUSION: With the Australian government promoting the role of PHCOs in health system reform, reflections from abroad highlight the key action within PHC and PHCOs required to optimise PHC workforce planning.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".