Natural Disasters and Service Delivery to Individuals with Severe Mental Illness—Ice Storm 1998
Bibliographic record
Abstract
OBJECTIVE: To review the literature on the responses of individuals with severe mental illness (SMI) to natural disasters, to describe the impact of the 1998 Ice Storm on a group of SMI patients, and to describe the steps taken at a Canadian university teaching hospital to ensure the ongoing provision of mental health services throughout the crisis. METHOD: Published articles describing the impact of natural disasters on SMI populations, as well as service provision to these patients, are reviewed. Service use at the Montreal General Hospital (MGH) Department of Psychiatry is described. A questionnaire about the impact of the ice storm was administered to a group of patients in an assertive community treatment program. RESULTS: Service use during this natural disaster was consistent with that described in the literature, in that these patients were no more likely to be admitted or to visit the emergency room during the crisis. Continuous mental health service delivery may have contributed to this positive outcome. This service delivery was provided by ensuring staff access to information, by securing the physical safety of both staff and patients, and by taking a flexible, outreach-oriented approach to service delivery. CONCLUSIONS: SMI patients who have ongoing access to psychiatric services in disaster situations tend to cope well. A flexible, proactive, assertive approach to service delivery during the crisis situation will help to ensure that needs for care will be met.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".