How institutional forces, ideas and actors shaped population health planning in Australian regional primary health care organisations
Bibliographic record
Abstract
BACKGROUND: Worldwide, there are competing norms driving health system changes and reorganisation. One such norm is that of health systems' responsibilities for population health as distinct from a focus on clinical services. In this paper we report on a case study of population health planning in Australian primary health care (PHC) organisations (Medicare Locals, 2011-2015). Drawing on institutional theory, we describe how institutional forces, ideas and actors shaped such planning. METHODS: We reviewed the planning documents of the 61 Medicare Locals and rated population health activities in each Medicare Local. We also conducted an online survey and 50 interviews with Medicare Local senior staff, and an interview and focus group with Federal Department of Health staff. RESULTS: Despite policy emphasis on population health, Medicare Locals reported higher levels of effort and capacity in providing clinical services. Health promotion and social determinants of health activities were undertaken on an ad hoc basis. Regulatory conditions imposed by the federal government including funding priorities and time schedules, were the predominant forces constraining population health planning. In some Medicare Locals, this was in conflict with the normative values and what Medicare Locals felt ought to be done. The alignment between the governmental and the cultural-cognitive forces of a narrow biomedical approach privileged clinical practice and ascribed less legitimacy to action on social determinants of health. Our study also shed light on the range of PHC actors and how their agency influenced Medicare Locals' performance in population health. The presence of senior staff or community boards with a strong commitment to population health were important in directing action towards population health and equity. CONCLUSIONS: There are numerous institutional, normative and cultural factors influencing population health planning. The experience of Australian Medicare Locals highlights the difficulties of planning in such a way that the impact of the social determinants on health and health equity are taken into account. The policy environment favours a focus on clinical services to the detriment of health promotion informed by a social determinants focus.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".