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
<i>Background:</i> 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. <i>Aim:</i> 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. <i>Method:</i> Data were obtained from an online sample of 1000 Canadian men (median age (<i>M</i>) = 49.63, standard deviation (<i>SD) </i>= 14.60). Confirmatory factor analysis (CFA) was used to replicate the established six-factor model of the MDRS-22. <i>Results:</i> 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%). <i>Conclusion:</i> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".