Development and Psychometric Properties of the Mental Health Knowledge Schedule
Bibliographic record
Abstract
OBJECTIVE: Stigma has been conceptualized as comprised of 3 constructs: knowledge (ignorance), attitudes (prejudice), and behaviour (discrimination). We are not aware of a psychometrically tested instrument to assess knowledge about mental health problems among the general public. Our paper presents the results of the development stage and the psychometric properties of the Mental Health Knowledge Schedule (MAKS), an instrument to assess stigma-related mental health knowledge among the general public. METHODS: We describe the development of the MAKS in addition to 3 studies that were carried out to evaluate the psychometric properties of the MAKS. Adults aged 25 to 45 years in socioeconomic groups: B, C1, and C2 completed the instrument via face-to-face interview (n = 92) and online (n = 403). RESULTS: Internal reliability and test-retest reliability is moderate to substantial. Validity is supported by extensive review by experts (including service users and international experts in stigma research). CONCLUSION: The lack of a valid outcome measure to assess knowledge is a shortcoming of evaluations of stigma interventions and programs. The MAKS was found to be a brief and feasible instrument for assessing and tracking stigma-related mental health knowledge. This instrument should be used in conjunction with other attitude- and behaviour-related measures.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".