A systematic review of mental health measurement scales for evaluating the effects of mental health prevention interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.079 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it