Psychometric properties of the French and English short form of the Protective Behavioural Strategies for Marijuana Scale in Canadian university students
Bibliographic record
Abstract
BACKGROUND: The Protective Behavioural Strategies for Marijuana (PBSM-17) scale serves to identify and measure strategies employed by young adults before, during or after cannabis use. After the adaptation and translation of the PBSM-17 into French, a methodological study was conducted to evaluate the psychometric properties of this French version (FV) and of the original English version (EV) in a sample of bilingual Canadian university students. METHODS: A total of 211 cannabis users (mean age=22.1 years) completed a sociodemographic questionnaire, a question on frequency of cannabis use (four categories: 1-3 times a month, once a week, more than once a week, everyday) and both versions (FV and EV) of the PBSM-17. RESULTS: Both versions had similar internal reliability (α=0.91; α=0.88). The one-factor solution explained 36.46% of the variance for the FV and 42.26% for the EV. As hypothesised, greater use of protective behavioural strategies was related to lower frequency of cannabis use. One-way ANOVA test results revealed a statistically significant difference in use of strategies by frequency of cannabis use for both the FV (F(3, 207)=27.38, p<0.001) and EV (F(3, 207)=29.32, p<0.001). Post hoc comparisons showed that everyday users employed fewer strategies on average than lower-frequency users. CONCLUSION: The FV and EV of the PBSM-17 demonstrated satisfactory psychometric properties. The proposed FV of the PBSM-17 is a reliable instrument that could be used for research and clinical purposes. Protective behavioural strategies can serve as indicator of lower-risk cannabis use and could be targeted in prevention interventions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".