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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".