Bibliographic record
Abstract
WHAT IS KNOWN ON THE SUBJECT?: Stigma involves connecting individuals with a particular label to negative characteristics; this is based not on the stigmatized condition itself, but cultural reactions to it. Stigma exists towards nurses with mental illness. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: This paper offers a first-person account of experiencing stigma as a nurse with a mental illness. This paper incorporates the existing literature to offer a broader cultural perspective on the experiences of a nurse with a mental illness. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Nurses are likely to encounter a nurse with a mental illness at some point in their practice. Nurses' reactions towards colleagues with mental illness can have significant implications for those colleague(s)' wellbeing. Nurses with mental illness will have to navigate their person and professional journey while giving consideration to the attitudes of their nursing peers and leaders. ABSTRACT: Limited research has been done on the stigma faced by nurses with mental illness from their nursing peers. Mental illness is not generally considered acceptable within the context of nursing culture, so when nurses do experience mental illness, their experiences in a professional context may be influenced by stereotypes, particularly those relating to dangerousness. Using autoethnography as a research method, the author examines her own subjective experiences of stigma as a nurse with a mental illness, and draws upon existing literature on stigma, deviance and the phenomenon of mental illness in nurses to analyse broader cultural implications for nursing. Assessment of suitability to return to work arises throughout the narratives, and consideration is given to the way that risk assessment by nursing leaders is impacted by negative stereotypes that surround mental illness.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| 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".