Predicting Behavioural Intentions to Those With Mental Illness: the Role of Attitude Specificity and Norms
Bibliographic record
Abstract
BACKGROUND: Social psychological research suggests that prediction of behavioural intentions towards those with mental illness could be increased by assessing attitudes towards specific actions or behaviours and by including a measure of perceived normative expectations by others concerning such behaviours. AIMS: To investigate whether attitudes towards specific behaviours and perceived normative expectations improve prediction of behavioural intentions towards a person with mental illness. METHODS: Two studies were carried out; one with university undergraduates and one with community service club members. Each included assessments of attitudes towards a person described as having a mental illness; attitudes towards specific behaviours reflecting social distance; perceived descriptive and injunctive norms with reference to those behaviours; and behavioural intentions. RESULTS: Attitude towards the specific behaviour frequently showed a higher correlation with behavioural intentions than did attitude towards the person. Inclusion of perceived norms also improved prediction of behavioural intention. CONCLUSIONS: The prediction of behavioural intentions towards those with mental illness may be improved by increasing the specificity of the attitude measures to the behavioural intentions being predicted and including measures of perceived norms. Furthermore, the effectiveness of efforts to reduce the stigma of mental illness may be increased by addressing their impact on perceived norms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".