Expectations about mental health symptom trajectories: associations with stigmatizing beliefs, helping behaviour, and social behaviour towards people diagnosed with schizophrenia
Bibliographic record
Abstract
Background Social support is crucial to recovery for many people diagnosed with schizophrenia, yet stigma poses a barrier to forming social relationships. The present study explored how beliefs about the trajectory of improvement, stability, or decline of schizophrenia symptoms (“trajectory beliefs”) are associated with stigmatizing behaviours (i.e. unwillingness to help and/or socialize with people diagnosed with schizophrenia).Methods Participants (N = 251) completed questionnaires regarding beliefs about people diagnosed with schizophrenia or depression (pertaining to trajectory, symptom stability, biogenetic attributions, exposure, and blame), respectively, and their willingness to interact (help and/or socialize) with people diagnosed with each diagnosis.Results Symptoms of schizophrenia were rated as more likely to worsen over time, whereas symptoms of depression were rated as more likely to improve. In multilevel mediation models, trajectory beliefs accounted for 25% and 23% of the differences in willingness to socialize and help between diagnoses, respectively. Trajectory beliefs about decline in schizophrenia were related to a lower willingness to engage in helping and socializing behaviours, and predicted willingness to help and socialize above and beyond perceptions of blame, biogenetic attributions, and past exposure to mental illness.Discussion Understanding how trajectory beliefs form, change, and affect behaviour has implications for stigma reduction and enhancing social support in schizophrenia.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".