An analysis of documentation language and word choice among forensic mental health nurses
Bibliographic record
Abstract
Documenting patient care is an important part of mental health services. The documentation is expected to be accurate, relevant, and informative for all future readers as it provides details about patients and the care they are receiving. Language can produce positive or negative emotions in individuals, and these emotions can influence their thoughts and actions. Considering this, nursing documentation can impact the future care of patients. In this study, our aim was to analyse the language and words nurses use when documenting about their patients. Through a qualitative review of notes transcribed by mental health nurses in a forensic setting (n = 55), we explored the adjectives and verbs used across a subsection of their documentation over a three-month period. More specifically, we identified the most common words used, looked for patterns in use, and examined the emotional weight - or valence - of word choice. Examination of valence scores of the adjectives and verbs in the notes indicates that while nurses describe their patients in a rather neutral manner overall, some words and phrases are ambiguous and/or repetitive, and have the potential to negatively influence the perceptions of the reader regarding the patient. Clinical implications of patient care are discussed in the context of bias management. Nurses need to consider how word choice is linked to negative prosody and the need to provide additional information to avoid ambiguity. Without such care, notes can be subject to misinterpretation by readers leading to undue labels, stigma, and bias.
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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 | 0.012 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".