Coordinating primary health care: an analysis of the outcomes of a systematic review
Bibliographic record
Abstract
OBJECTIVES: To identify the types of strategy used to coordinate care within primary health care (PHC) and between PHC, health services and health-related services in Australia and other countries that have comparable health systems, and to describe what is known about their effectiveness; to review the implications for health policy and practice in Australia. METHODS: We conducted a systematic review of the literature (January 1995 to March 2006) relating to care coordination in Australia, the United States, the United Kingdom, New Zealand, Canada and The Netherlands. Our review was supplemented by consultations with academic experts and policymakers. RESULTS: Six types of strategy were identified at patient/provider level, falling into two groups: (i) communication and support for providers and patients, and (ii) structural arrangements to support coordination. These were broadly consistent with existing typologies. All were associated with improved health and/or patient satisfaction outcomes in more than 50% of studies, and interventions using multiple strategies were more successful than those using single strategies. CONCLUSIONS: The largely incremental approach to improving coordination of care in Australia has involved a broad range of strategy types but has also perpetuated existing structural problems. Reforms in governance, funding and patient registration in primary health care would provide a stronger base for effective care coordination.
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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 | 0.094 | 0.303 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.012 |
| Bibliometrics | 0.034 | 0.052 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".