How forensic mental health nurses’ perspectives of their patients can bias healthcare: A qualitative review of nursing documentation
Bibliographic record
Abstract
AIMS AND OBJECTIVES: Our aim was to examine the notes produced by nurses, paying specific attention to the style in which these notes are written and observing whether there are concerns of distortions and biases. BACKGROUND: Clinicians are responsible to document and record accurately. However, nurses' attitudes towards their patients can influence the quality of care they provide their patients and this inevitably impacts their perceptions and judgments, with implications to patients' care, treatment, and recovery. Negative attitudes or bias can cascade to other care providers and professionals. DESIGN: This study used a retrospective chart review design and qualitative exploration of documentation using an emergent theme analysis. METHODS: We examined the notes taken by 55 mental health nurses working with inpatients in the forensic services department at a psychiatric hospital. The study complies with the SRQR Checklist (Appendix S1) published in 2014. RESULTS: The results highlight some evidence of nurses' empathic responses to patients, but suggest that most nurses have a style of writing that much of the time includes themes that are negative in nature to discount, pathologise, or paternalise their patients. CONCLUSIONS: When reviewing the documentation of nurses in this study, it is easy to see how they can influence and bias the perspective of other staff. Such bias cascade and bias snowball have been shown in many domains, and in the context of nursing it can bias the type of care provided, the assessments made and the decisions formed by other professionals. RELEVANCE TO CLINICAL PRACTICE: Given the critical role documentation plays in healthcare, our results indicate that efforts to improve documentation made by mental health nurses are needed and specifically, attention needs to be given to the writing styles of the notation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".