SÉCuRE: A clinical tool for comprehensively assessing home safety of people with mental illness
Bibliographic record
Abstract
Introduction People with severe mental illness benefit from a growing number of measures aimed at supporting independent housing. The purpose of the study was to develop a comprehensive home safety assessment tool. Method The tool's elaboration was done in three phases. The planning phase was intended to circumscribe the phenomenon, providing input for the development phase, which consisted of creating and enhancing the tool's prototypes. The evaluation phase then featured the tool's translation validity (relevance, exhaustiveness, clarity, and apparent clinical utility), with four successive rounds of expert consultation ( n = 20). Changes were made to the tool according to the experts' suggestions. Findings The proposed tool, SÉCuRE, adopts a structured professional judgment approach that is designed to be used collaboratively and interprofessionally, with a specific role for occupational therapists. It aims to systematize the assessment of contributive factors (risk and protective), all stakeholders' expectations and needs and the identification of potential ethical issues. The findings supported the translation validity and acceptance of the tool by clinicians. Conclusion SÉCuRE was developed to assist with clinical judgment regarding home safety interventions. It is hoped that its use may ultimately foster home safety in the context of recovery.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".