Psychometric Assessment of the Mental Health Self-Management Questionnaire in a Clinical Sample With Anxiety Disorders
Bibliographic record
Abstract
The development of self-management strategies plays an active role in the recovery process of individuals suffering from mental disorders. The Mental Health Self-Management Questionnaire (MHSQ) was developed to give empirical insight into the construct of self-management. This study aimed to assess the psychometric properties of this recent instrument in a sample of patients meeting DSM-5 criteria for anxiety disorders. Data were drawn from a randomized controlled trial examining transdiagnostic group cognitive-behavioural therapy compared to treatment-as-usual for anxiety disorders. Participants ( n = 231) completed a structured interview for anxiety disorders and several self-reported measures, including the MHSQ. Confirmatory factor analysis for the initial three-factor structure showed adequate fit after adding covariances between certain items. The MHSQ had acceptable reliability for the clinical (α = .73, ω = .78), empowerment (α = .80, ω = .80) and vitality (α = .71, ω = .71) subscales. Results showed high correlations with measures of well-being and moderate correlations with measures of disability and depression, especially on the empowerment and vitality subscales. Lower correlations were found between the MHSQ and anxiety symptoms. The linear mixed model examining sensitivity to change showed that the MHSQ score at post-treatment was significantly greater for the experimental condition compared to the control condition for the clinical and empowerment subscales. The MHSQ is a promising measure, and research with other clinical populations and long-term follow-up is warranted to gather further evidence on the validity and fidelity of the instrument.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".