Responding to the needs of migrants during the COVID-19 pandemic in Montreal and Sherbrooke: Analysis of the influence strategies of actors involved in intersectoral collaborations
Bibliographic record
Abstract
This article looks at the influence strategies of actors involved in intersectoral collaborations set up during the COVID-19 pandemic to influence collective decisions. It is part of a participatory research project that sets out to analyze the emergence and implementation of intersectoral initiatives between the community and public services during COVID-19 to better meet the needs of refugees, asylum seekers and non-status migrants in Montreal and Sherbrooke. The data analyzed in this case study were collected from 28 front-line workers and managers in the health sector (8), the community (19) and another public sector (1). The results show that the actors involved in collaborations mobilized two types of strategies—intersectoral and intra-sectoral—to influence collective decisions. On the whole, the strategies developed in times of crisis are more or less the same as those used in non-crisis periods. The difference lies in the mechanisms (context, sources of power) on which they are based. Most of the influence strategies deployed during the pandemic were based on the context of urgency and turbulence in which collaboration took place. This study documents how intersectoral collaborations work in the context of crisis, making an important contribution to the theory and practice of intersectoral collaborations. Points for practitioners In a crisis context, intersectoral collaboration is strengthened by the pooling of resources and the management of organizational interests through compromise. Public managers must promote transparency, institutional openness and dialogue to reconcile collective and organizational interests. These practices foster partner engagement, prevent strategic withdrawals and maximize the impact of joint actions, while preparing actors to work together more effectively in future crises.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".