Exploring the implementation and underlying mechanisms of centralized referral systems to access specialized health services in Quebec
Bibliographic record
Abstract
BACKGROUND: In 2016, Quebec, a Canadian province, implemented a program to improve access to specialized health services (Accès priorisé aux services spécialisés (APSS)), which includes single regional access points for processing requests to such services via primary care (Centre de répartition des demandes de services (CRDS)). Family physicians fill out and submit requests for initial consultations with specialists using a standardized form with predefined prioritization levels according to listed reasons for consultations, which is then sent to the centralized referral system (the CRDS) where consultations with specialists are assigned. We 1) described the APSS-CRDS program in three Quebec regions using logic models; 2) compared similarities and differences in the components and processes of the APSS-CRDS models; and 3) explored contextual factors influencing the models' similarities and differences. METHODS: We relied on a qualitative study to develop logic models of the implemented APSS-CRDS program in three regions. Semi-structured interviews with health administrators (n = 9) were conducted. The interviews were analysed using a framework analysis approach according to the APSS-CRDS's components included in the initially designed program, Mitchell and Lewis (2003)'s logic model framework, and Chaudoir and colleagues (2013)'s framework on contextual factors' influence on an innovation's implementation. RESULTS: Findings show the APSS-CRDS program's regional variability in the implementation of its components, including its structure (centralized/decentralized), human resources involved in implementation and operation, processes to obtain specialists' availability and assess/relay requests, as well as monitoring methods. Variability may be explained by contextual factors' influence, like ministerial and medical associations' involvement, collaborations, the context's implementation readiness, physician practice characteristics, and the program's adaptability. INTERPRETATION: Findings are useful to inform decision-makers on the design of programs like the APSS-CRDS, which aim to improve access to specialists, the essential components for the design of these types of interventions, and how contextual factors may influence program implementation. Variability in program design is important to consider as it may influence anticipated effects, a next step for the research team. Results may also inform stakeholders should they wish to implement similar programs to increase access to specialized health services via primary care.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".