Exploring the Knowledge, Attitudes, and Behavioural Responses of Healthcare Students towards Mental Illnesses—A Qualitative Study
Bibliographic record
Abstract
Background: The stigma of mental illness causes delays in seeking help, and often compromises victims’ therapeutic relationships with healthcare providers. The knowledge, attitudes, and behavioural responses of future healthcare professionals toward individuals with mental illnesses are explored here to suggest steps that will reduce mental illness stigma in healthcare providers. Methods: A generic qualitative approach—Qualitative Description—was used. Eighteen students from nine healthcare programs at a Canadian University participated in individual semi-structured interviews. Participants answered questions regarding their knowledge, attitudes, and behavioural responses towards individuals with mental illnesses. Thematic content analysis guided the data analysis. Results: Four main themes were constructed from the data: positive and negative general perceptions toward mental illness; contact experiences with mental illnesses; mental illness in a healthcare setting; and learning about mental illness in healthcare academia. Conclusions: Students showed well-rounded mental health knowledge and mostly positive behaviours toward individuals with mental illnesses. However, some students hold stigmatizing attitudes and do not feel prepared through their academic experiences to work with individuals with mental illnesses. Mental health education can reduce the stigma toward mental illness and improve the care delivered by healthcare professionals.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".