Canadian French Translation and Preliminary Validation of the Conformity to Masculine Norms Inventory: A Pilot Study
Bibliographic record
Abstract
Conformity to masculine norms has been linked to poor mental and physical health outcomes. Its valid assessment among subgroups of the population is therefore a crucial step in the investigation of intercultural variability in the enactment of masculinity, as well as its causes, costs, and benefits. The present pilot study aimed to adapt and conduct a preliminary validation of a French version of the Conformity to Masculine Norms Inventory (CMNI-22), a self-report questionnaire designed to assess overall conformity to male gender standards. The French adaptation of the CMNI-22 (CanFr-CMNI-22) was developed using a forward-backward translation process. The data from a sample of 57 Canadian French men (23-81 years old), collected at two time points 2 weeks apart, were then analyzed to investigate the psychometric properties and factor structure of the CanFr-CMNI-22. Findings indicated adequate internal reliability of the global scores and highly satisfactory test-retest reliability. Correlations with the Male Role Norms Inventory-Short Form (MRNI-SF) at both time points also showed strong convergent validity. Overall, the CanFr-CMNI-22 appears to be a reliable and valid instrument to assess conformity to traditional masculine gender norms in French-speaking men from the general population. This study is a key step in a research process aiming to validate the Canadian French version of the CMNI and contributes to enhance inclusive research and clinical care to foster men's health.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".