The development of a national bilingual cross-sectional questionnaire on attitudes towards supervised consumption sites and e-health overdose response interventions in Canada: The Canadian National Questionnaire on Overdose Monitoring (CNQOM)
Bibliographic record
Abstract
ABSTRACT Background Supervised consumption sites (SCSs) and overdose response hotlines/applications (ORHAs) are harm reduction interventions aimed at reducing fatal overdose mortality. Little is known about key informant perspectives regarding these services. Herein, our objective is to describe the process of developing, testing, and distributing a novel national online questionnaire to measure perspectives and attitudes towards SCS and ORHAs among key informants in Canada. Methods A bilingual online instrument (the Canadian National Questionnaire on Overdose Monitoring, CNQOM) was developed, pilot tested and revised with the aim of ensuring content and construct validity. Questionnaire respondents were recruited nationally from four key informant groups (People Who Use Substances, health professionals, emergency responders, and the general public) using a mix of purposive and representative sampling strategies. Respondents came from every province and territory in Canada, and respondents from the general public were proportionally represented. A stepwise response validation approach was used to remove invalid questionnaire responses. Test-retest reliability of instrument questions was assessed using Spearman’s Rank Correlation, the Wilcoxon Rank Sum Test, and Cohen’s Kappa. Results A total of 4,445 valid responses was obtained from the four key informant groups following data cleaning. Test-retest reliability of instrument questions demonstrated slight to substantial stability in responses. Discussion The CNQOM is the first online questionnaire in Canada designed to capture perspectives and attitudes towards SCSs and ORHAs among diverse key informant groups. Our questionnaire was administered to a large, geographically diverse sample and designed to capture the perspectives of four key informant groups. Lower than expected test-retest reliability may be explained by lack of participant familiarity with SCS and especially ORHAs and the impersonal nature of the instrument content among some respondents. Future work will elucidate key informant perspectives on these services based on the data.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".