Stigma, negative attitudes and discrimination towards mental illness within the nursing profession: a review of the literature
Bibliographic record
Abstract
The aim of this paper was to review the existing literature pertaining to stigma, negative attitudes and discrimination towards mental illness, specifically as viewed through the lens of the nursing profession. The results of the literature review were synthesized and analysed, and the major themes drawn from this were found to correspond with Schulze's model identifying three positions that healthcare workers may assume in relation to stigma of mental illness: 'stigmatizers', 'stigmatized' and 'de-stigmatizers'. In this paper, the nursing profession is examined from the perspectives of the first two major themes: the 'stigmatizers' and 'stigmatized'. Their primary sub-themes are identified and discussed: (1) Nurses as 'the stigmatizers': (a) nurses' attitudes in general medical settings towards patients with psychiatric illness and (b) psychiatric nurses; (2) Nurses as 'the stigmatized': (a) nurses who have mental illness and (b) stigma within the profession against psychiatric nurses and/or psychiatry in general. The secondary and tertiary sub-themes are also identified and reviewed.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".