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Record W4229000969 · doi:10.1111/jocn.16342

Exploration of note writing by mental health nurses using a video scenario

2022· article· en· W4229000969 on OpenAlexaff
Krystle Martin, Korri Bickle, Rosemary Ricciardelli, Jessica Lok

Bibliographic record

VenueJournal of Clinical Nursing · 2022
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsMemorial University of NewfoundlandOntario Tech UniversityOntario Shores Centre for Mental Health Sciences
Fundersnot available
KeywordsDocumentationPsychologyProcess (computing)Quality (philosophy)Content analysisMental healthNursingHealth careNursing processMental health nursingMedical educationMedicineComputer sciencePsychiatrySociology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.081
GPT teacher head0.480
Teacher spread0.398 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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