Primary Care Transformation During a Pandemic: Rapid Reforms Focused on Outreach Approaches and Intersectoral Collaboration to Better Serve Vulnerable Populations
Bibliographic record
Abstract
This article examines how existing primary care services were transformed in Québec during COVID-19 to better serve the most vulnerable individuals for whom inequities and access difficulties increased during the pandemic. In the context of a research project, six particularly promising practices to respond to these challenges were identified within one Centre intégré universitaire de santé et de services sociaux (CIUSSS). Using van Gestel et al.’s (2018) framework, which focuses on timing, ideas and institutional contexts, these practices are analyzed as rapid reforms, that is, policy responses or innovations that are initiated at an unusual pace in high pressure contexts, such as the COVID-19 pandemic, that provide an unprecedented window of opportunity to transform primary care services. The extreme pressure exerted on politicians and public decision-makers to act quickly created a context, characterized in certain circumstances by a decentralization of decision-making in the health system and greater agency by frontline actors, favouring bottom-up innovations. Despite the emergence of various rapid reforms, certain longer-term questions arise regarding their potential for sustainability, because their implementation has not been based on an in-depth redefinition of institutional structures and logics, which rests on the long-term adoption of new norms and values. Cet article examine comment les services de première ligne ont été transformés au Québec pendant la COVID-19 afin de mieux desservir les clientèles vulnérables, pour lesquels les inégalités et les difficultés d'accès se sont accrues pendant la pandémie. Dans le cadre d'un projet de recherche, six pratiques particulièrement prometteuses en réponse à ces enjeux ont été identifiées au sein d'un Centre intégré universitaire de santé et de services sociaux (CIUSSS). En utilisant le cadre de van Gestel et al. (2018), qui s’attarde au timing, aux idées et aux contextes institutionnels, ces pratiques sont analysées comme des réformes rapides, c'est-à-dire des politiques ou des innovations initiées à un rythme inhabituel dans un contexte de fortes pressions, tel que la pandémie de la COVID-19, et offrant une fenêtre d'opportunité sans précédent pour transformer les services de première ligne. La pression extrême exercée sur les politiciens et les décideurs publics pour qu'ils agissent rapidement a créé un contexte, caractérisé dans certaines circonstances par une décentralisation de la prise de décision dans le système de santé et une plus grande capacité d’action des acteurs sur le terrain, favorisant les innovations « du bas vers le haut » (bottom-up). Malgré l'émergence de ces réformes rapides, certaines questions à plus long terme se posent quant à leur potentiel de pérennisation, car leur mise en œuvre n'a pas été fondée sur une redéfinition en profondeur des structures et logiques institutionnelles, qui repose sur l'adoption à plus long terme de nouvelles normes et valeurs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".