Primary healthcare organizations facing a disaster: the Quebec experience
Bibliographic record
Abstract
Purpose This paper seeks to evaluate the role and the nature of the interventions by primary healthcare organizations in Quebec during the ice storm of 1998. The two basic questions are: to what extent CLSC perform their socio‐community mission during this disaster? Are there some contingencies factors that explain variation between CLSC in their intervention? Design/methodology/approach Data collection is based on face to face discussion held with 19 managers responsible for the disaster's plan for their CLSC, 59 professionals that took an active and direct role in disaster interventions and 25 collaborators from municipalities and diverse community organisms. On top of the face to face discussions, a questionnaire was remitted to all the interviewees. Findings The research provides a qualified analysis of the interventions of the CLSCs and identifies what works well (performance factors), what went wrong (non performance factors) and what varies from one territory to another (variance or contingencies factors). The results of this research corroborate most of the reports brought about by the theories of disaster management planning to the look at the importance of plans, the development of a culture of prevention and of civil security, the importance of the training as much for the leaders as for the professionals. Moreover, primary healthcare organizations such as CLSC's in Quebec have a strategic positioning in disaster because of the multidisciplinary functioning of their teams and their knowledge of the local community. Research limitations/implications The research is limited to the interventions of CLSCs and the interventions of other professionals of the health system or from volunteers are not considered. Practical implications The social community mission of organizations such as the CLSC can be optimized using four specific plans: the clientele plan, the training plan, the local consultation plan and the multidisciplinary plan. Specific recommendations on each aspect are identified to help managers and professionals in the attainment of their socio‐communitarian mission. Originality/value The research provides interesting data on the contribution of multidisciplinary teams (nurses, doctors, social workers, psychologists, ergotherapists, etc.) as well as from their managers and first line collaborators. It puts in evidence their strengths and weaknesses and identifies ways of improving disaster interventions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".