Exploration of note writing by mental health nurses using a video scenario
Bibliographic record
Abstract
AIMS AND OBJECTIONS: We aimed to explore the content and language of nursing documentation and gain insight into the internal processes of nurses while notetaking. BACKGROUND: Documentation is a core competency of mental health nursing, has clinical and ethical importance and is the integral to the efficient and effective care provided to patients. However, issues related to the content and quality of nursing notes continues to be a concern and there remains gaps in our understanding about the internal processes that nurses engage in when writing notes. DESIGN: We used a mixed method design that included a content analysis with note review and interviews. METHODS: After watching a video, psychiatric nurses (n = 27) wrote a note and then were interviewed about their note taking process. We used the COREQ guidelines for reporting our data. RESULTS: Participants relied on four main themes when determining what to include in their notes-what happened and what the patient said or did, plus anything different than baseline, and safety concerns. Analyses revealed the presence of bias in the notetaking and participants were not familiar with effective strategies to mitigate these during the documentation process. Lastly, we found that notes are typically consistent in using some of the SOAPE format with notes focused on direct observations and the use of 'facts', while assessment and construction of treatment plans are used to a lesser extent. CONCLUSIONS: Our results provide insight into the decision-making process of nursing staff regarding their documentation practices: overall they appear unaware of the importance of their notes, and believe that capturing the facts about their patients is important, while devaluing their own input and interpretations. RELEVANCE TO CLINICAL PRACTICE: Our results provide evidence that mental health nurses may need additional training regarding documentation, more specifically about what to include, word choice 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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".