SURPS French version validation in a Quebec adolescent population
Bibliographic record
Abstract
Objective: The Substance Use Risk Profile Scale (SURPS) has been developed to screen personality risk factors for substance misuse. This scale assesses 4 high-risk personality traits using a 23-item, self-report questionnaire, SURF'S helps guiding targeted approaches to prevention of substance abuse and misuse. It has been validated in the United Kingdom, English Canada, Sri Lanka, and China. This study aims to validate this scale in a sample of French-speaking adolescents from Quebec as well as its sensitivity in a clinical sample of adolescents. Method: Two hundred two 15-year-old youths from a community sample completed a French version of SURPS as well as other measures of personality and substance use. This study reports the internal consistency and concurrent validity of the scale, as well as a factor analysis of items, Further, 40 youths (mean age 15,7 years) from a clinical population completed SURF'S and their scores were compared with those of the community sample. Results: SURPS French translation has good internal consistency and demonstrated a 4-factor structure very similar to the original scale. The 4 subscales show good concurrent validity, and 3 of the subscales were found to correlate with measures of substance use. Finally, 95% of the clinical sample was identified at high risk for substance misuse according to SURPS cut-off scores. Conclusion; SURPS French translation seems to be a valid and sensitive scale that can be used in a French-speaking adolescent population from Quebec.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".