Validity of the Male Depression Risk Scale in a representative Canadian sample: sensitivity and specificity in identifying men with recent suicide attempt
Bibliographic record
Abstract
Background: Clinical practice and literature has supported the existence of a phenotypic sub-type of depression in men. While a number of self-report rating scales have been developed in order to empirically test the male depression construct, psychometric validation of these scales is limited. Aim: To confirm the psychometric properties of the multidimensional Male Depression Risk Scale (MDRS-22) and to develop clinical cut-off scores for the MDRS-22. Method: Data were obtained from an online sample of 1000 Canadian men (median age (M) = 49.63, standard deviation (SD) = 14.60). Confirmatory factor analysis (CFA) was used to replicate the established six-factor model of the MDRS-22. Results: Psychometric values of the MDRS subscales were comparable to the widely used Patient Health Questionnaire-9. CFA model fit indices indicated adequate model fit for the six-factor MDRS-22 model. ROC curve analysis indicated the MDRS-22 was effective for identifying those with a recent (previous four-weeks) suicide attempt (area under curve (AUC) values = 0.837). The MDRS-22 cut-off identified proportionally more (84.62%) cases of recent suicide attempt relative to the PHQ-9 moderate range (53.85%). Conclusion: The MDRS-22 is the first male-sensitive depression scale to be psychometrically validated using CFA techniques in independent and cross-nation samples. Additional studies should identify differential item functioning and evaluate cross-cultural effects.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".