Mandated Local Health Networks across the province of Québec: a better collaboration with primary care working in the communities?
Bibliographic record
Abstract
Background In 2004, the Québec government implemented an important reform of the healthcare system. The reform was based on the creation of new organisations called Health Services and Social Centres (HSSC), which were formed by merging several healthcare organisations. Upon their creation, each HSSC received the legal mandate to establish and lead a Local Health Network (LHN) with different partners within their territory. This mandate promotes a 'population-based approach' based to the responsibility for the population of a local territory. Objective The aim of this paper is to illustrate and discuss how primary healthcare organisations (PHC) are involved in mandated LHNs in Québec. For illustration, we describe four examples that facilitate a better understanding of these integrated relationships. Results The development of the LHNs and the different collaboration relationships are described through four examples: (1) improving PHC services within the LHN - an example of new PHC models; (2) improving access to specialists and diagnostic tests for family physicians working in the community - an example of centralised access to specialists services; (3) improving chronic-disease-related services for the population of the LHN - an example of a Diabetes Centre; and (4) improving access to family physicians for the population of the LHN - an example of the centralised waiting list for unattached patients. Conclusion From these examples, we can see that the implementation of large-scale reform involves incorporating actors at all levels in the system, and facilitates collaboration between healthcare organisations, family physicians and the community. These examples suggest that the reform provided room for multiple innovations. The planning and organisation of health services became more focused on the population of a local territory. The LHN allows a territorial vision of these planning and organisational processes to develop. LHN also seems a valuable lever when all the stakeholders are involved and when the different organisations serve the community by providing acute care and chronic care, while taking into account the social, medical and nursing fields.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".