Explaining variation of implementation outcomes of centralized waiting lists for unattached patients
Bibliographic record
Abstract
PURPOSE: Centralized waiting lists (CWLs) for patient attachment to a primary care provider have been implemented across Canada, including Quebec. Little is known about the implementation of CWLs and the factors that influence implementation outcomes of such primary care innovations. The purpose of this paper is to explain variations in the outcomes of implementation by analyzing the characteristics of CWLs and contextual factors that influence their implementation. DESIGN/METHODOLOGY/APPROACH: A multiple qualitative case study was conducted. Four contrasting CWLs were purposefully selected: two relatively high-performing and two relatively low-performing cases with regard to process indicators. Data collected between 2015 and 2016 drew on three sources: 26 semi-structured interviews with key stakeholders, 22 documents and field notes. The Consolidated Framework for Implementation Research was used to identify, through a cross-case comparison of ratings, constructs that distinguish high from low-performing cases. FINDINGS: Five constructs distinguished high from low-performing cases: three related to the inner setting: network and communications; leadership engagement; available resources; one from innovation characteristics: adaptability with regard to registration, evaluation of priority and attachment to a family physician; and, one associated with process domain: engaging. Other constructs exerted influence on implementation (e.g. outer setting, individual characteristics), but did not distinguish high and low-performing cases. ORIGINALITY/VALUE: This is the first in-depth analysis of CWL implementation. Results suggest important factors that might be useful in efforts to continuously improve implementation performance of CWLs and similar innovations.
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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 | 0.031 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".