Responses to Mental Health Stigma Questions: The Importance of Social Desirability and Data Collection Method
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact on the general public of England's Time to Change program to reduce mental health-related stigma and discrimination using newly developed measures of knowledge and intended behaviour regarding people with mental health problems, and an established attitudes scale, and to investigate whether social desirability affects responses to the new measures and test whether this varies according to data collection method. METHOD: The Mental Health Knowledge Schedule (MAKS) and Reported and Intended Behaviour Scale (RIBS) were administered together with the 13-item version of the Marlowe-Crowne Social Desirability Scale to 2 samples (each n = 196) drawn from the Time to Change mass media campaign target group; one group was interviewed face to face, while the other completed the measures as an online survey. RESULTS: After controlling for other covariates, interaction terms between collection method and social desirability were positive for each instrument. The social desirability score was associated with the RIBS score in the face-to-face group only (β = 0.35, 95% CI 0.14 to 0.57), but not with the MAKS score in either group; however, MAKS scores were more likely to be positive when data were collected face to face (β = 1.53, 95% CI 0.74 to 2.32). CONCLUSIONS: Behavioural intentions toward people with mental health problems may be better assessed using online self-complete methods than in-person interviews. The effect of face-to-face interviewing on knowledge requires further investigation.
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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 | 0.200 | 0.353 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".