Implementing care and service pathways before and after COVID: what changes, what stays? A multiple case study in Quebec, Canada.
Bibliographic record
Abstract
Since 206, Quebec's health and social services institutions have developed care and service pathways to improve access and continuity. Each institution created specific implementation strategies and management models. The pandemic disrupted these efforts, in different ways for each institution and its environment.Our presentation proposes to explore the zones of tension between the initial conditions and the current conditions (post-pandemic recovery context) of the implementation of the pathways. What has (not) changed in the implementation strategy, what has (not) changed in the implementation context? What are the coherences and incoherences between the changes in the environment and the changes in the practices?Our research team has been monitoring and evaluating the implementation in 4 institutions since 207 (in real time and retrospectively). Data were collected through semi-directed interviews with key actors (n=42) at operational, tactical and strategic levels in 4 health and social services institutions. The analysis was based on the conceptual framework of collaborative governance developped by Bryson, Crosby and Stone (2006) and on the model of robust governance strategies in a turbulent environment developed by Ansell, Sorensen and Torfing (2020).Our data show different strategies : One institution tried to use the same pre-COVID strategy, which had already shown its limitations, to implement pathways in a post-pandemic recovery context; another completely changed its strategic orientation and abandoned the implantation of pathways as they were previously conceptualized; another tried a new way of implementing pathways, but conceptualized in the same way; the last continued to implement in a very different way from the others, reinforcing its solo implementation strategy. Our analyses show that the situations are different depending on the environment and conditions of each institution: its governance structure and process, the turnover (or not) of key actors, the strategic vision and concrete practices in terms of (matrix) governance, the previous collaboration with partners, and the management practices during the COVID period.This study draws lessons on how health systems can (and must) implement adaptable interventions that evolve in contexts that are bound to change in time and space. This project is part of a multi-year international program. Matrix management and collaborative governance are at the heart of trajectory management and this program. The next steps will be to document the differentiated effects of these implementation strategies on the performance outcomes of healthcare organizations.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".