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.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".