A systematic review of mental health measurement scales for evaluating the effects of mental health prevention interventions
Bibliographic record
Abstract
BACKGROUND: Consistent and appropriate measurement is needed in order to improve understanding and evaluation of preventative interventions. This review aims to identify individual-level measurement tools used to evaluate mental health prevention interventions to inform harmonization of outcome measurement in this area. METHODS: Searches were conducted in PubMed, PsychInfo, CINAHL, Cochrane and OpenGrey for studies published between 2008 and 2018 that aimed to evaluate prevention interventions for common mental health problems in adults and used at least one measurement scale (PROSPERO CRD42018095519). For each study, mental health measurement tools were identified and reviewed for reliability, validity, ease-of-use and cultural sensitivity. RESULTS: A total of 127 studies were identified that used 65 mental health measurement tools. Most were used by a single study (57%, N = 37) and measured depression (N = 20) or overall mental health (N = 18). The most commonly used questionnaire (15%) was the Centre for Epidemiological Studies Depression Scale. A further 125 tools were identified which measured non-mental health-specific outcomes. CONCLUSIONS: There was little agreement in measurement tools used across mental health prevention studies, which may hinder comparison across studies. Future research on measurement properties and acceptability of measurements in applied and scientific settings could be explored. Further work on supporting researchers to decide on appropriate outcome measurement for prevention would be beneficial for the field.
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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 | 0.033 | 0.140 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".