A narrative review and synthesis to inform health workforce preparation for the Health Care Homes model in primary healthcare in Australia
Bibliographic record
Abstract
The Australian Government Health Care Homes (HCH) model recently implemented in general practice targets people with chronic complex conditions. Identifying how general practitioners (GPs) and practice nurses (PNs) can work within this model is important given existing health workforce challenges. A narrative review and synthesis has been undertaken to develop a preliminary understanding of this, incorporating literature describing health workforce challenges, GP and PN functions, and team-based care; supplemented by interviews with key informants from within the primary healthcare system. Narrative synthesis principles guided literature analysis. Interview data were thematically analysed. A clear rationale for health workforce reform was ascertained and functions for the GP and PN under the HCH model were determined. The model was found to be an opportunity for an enhanced PN role in a team-based approach to care with the GP. Challenges to advancing the PN role and team-based care were identified, including the medical dominance of the health system and the significant culture change required by general practices to fully implement the model. Enablers included strong nursing leadership and improved ongoing education for PNs to unlock their capacity. The HCH model is an opportunity to strengthen primary healthcare, provided concerted action is taken regarding these challenges and enablers.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".