Attitudes of health professionals towards people with substance use disorders in Brazil, controlling for the effects of social desirability
Bibliographic record
Abstract
Health professionals are in a strategic position to help people with substance use disorders (SUDs) who seek health services for support or treatment. However, it is known that professionals' attitudes towards people who use alcohol and other drugs are marked by stigmatizing attitudes that create barriers to access quality treatment and make it difficult for the user to adhere to it. From this, the present study aimed to investigate the attitudes of 264 health professionals from specialised services and primary health care (PHC) in the Southeast region of Brazil, through Opening Minds Scale for Healthcare Providers (OMS-HC), taking into account the hypothesis of contact with the subject as a predictor of more positive attitudes. For this, a Multiple Hierarchical Regression was carried out to ascertain the contribution of the variables used in the explanatory model of attitudes. In addition, the measure of social desirability (SD), assessed by Brazilian Portuguese adaptation of Marlowe-Crowne Social Desirability Scale, was used as a control variable in the regression model to obtain a more robust and accurate model regarding the presence of biased responses, pervasive in studies on attitudes. In general, our findings showed that contact/familiarity with substance use, either through direct contact with users or through the respondent's own use, predicted more positive attitudes, with specialised service professionals expressing more positive attitudes than those working in PHC. Blaming the user for his/her condition presented itself as a predictor of more negative attitudes. Studies like this are of paramount importance for understanding the relationship established between professionals and service users and, therefore, for tailoring interventions and programs that aim to reduce stigmatizing attitudes and provide better access to health for people with SUDs. The importance of using the SD measure as a control variable in Regression is also emphasised, as an effective way to overcome to a common limitation in studies of attitudes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".