What is the difference between comprehensive and selective primary health care? Evidence from a five-year longitudinal realist case study in South Australia
Bibliographic record
Abstract
BACKGROUND: Since the WHO's Alma Ata Declaration on Primary Health Care (PHC) there has been debate about the advisability of adopting comprehensive or selective PHC. Proponents of the latter argue that a more selective approach will enable interim gains while proponents of a comprehensive approach argue that it is needed to address the underlying causes of ill health and improve health outcomes sustainably. METHODS: This research is based on four case studies of government-funded and run PHC services in Adelaide, South Australia. Program logic models were constructed from interviews and workshops. The initial model represented relatively comprehensive service provision in 2010. Subsequent interviews in 2013 permitted the construction of a selective PHC program logic model following a series of restructuring service changes. RESULTS: Comparison of the PHC service program logic models before and after restructuring illustrates the changes to the operating context, underlying mechanisms, service qualities, activities, activity outcomes and anticipated community health outcomes. The PHC services moved from focusing on a range of community, group and individual clinical activities to a focus on the management of people with chronic disease. Under the more comprehensive model, activities were along a continuum of promotive, preventive, rehabilitative and curative. Under the selective model, the focus moved to rehabilitative and curative with very little other activities. CONCLUSION: The study demonstrates the difference between selective and comprehensive approaches to PHC in a rich country setting and is useful in informing debates on PHC especially in the context of the Sustainable Development Goals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".