interRAI Subjective Quality of Life Scale for Mental Health and Addiction Settings: A Self-Reported Measure Developed From a Multi-National Study
Bibliographic record
Abstract
Background: Measuring Quality of Life (QoL) in mental health using self-reported items is important for evaluating the quality of service and understanding the person's experience of the care received. Objective: The aim of this research was to develop and validate a self-reported QoL instrument for inpatient and community mental health settings. Methods: Data were collected from diverse research sites in Canada, Belgium, Russia, Finland, Brazil, and Hong Kong, using the 37-item interRAI Quality of Life Survey for Mental Health and Addictions. The survey was administrated to 2,218 participants from inpatient and community mental health settings, assisted living, and the general community. We randomly divided the sample into a training and a test sample (70 and 30%, respectively). We conducted principal component analysis (PCA) and exploratory factor analysis (EFA) using the training sample to identify potential factor structure. Confirmatory factor analysis (CFA) models were then fitted to finalize and externally validate the measurement model using training and test data, respectively. Results: PCA, EFA, and CFA of the training sample collectively suggested a 23-item scale measuring four latent constructs: well-being and hope (8 items), relationship (7 items), support (5 items), and activity (3 items). This model was supported by the CFA of the test sample. The goodness-of-fit statistics root mean square error, comparative fit index and Tucker-Lewis index were 0.03, 1.00, and 0.99, respectively. Estimated Cronbach's alpha based on the test data was 0.92. Raw Cronbach's alpha values for the subscales were 0.86 for well-being and hope, 0.86 for relationship, 0.69 for support, and 0.72 for activity. Conclusions: The interRAI SQoL-MHA scale is a valid instrument to measure QoL in mental health settings. The instrument will support the evaluation of the quality of care and can also be used for future research to produce SQoL-MHA values on a quality adjusted-life-year scale, facilitating the evaluation of various mental health interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".