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Record W2471501953 · doi:10.1111/jpm.12314

Mental health nurses’ views about antipsychotic medication side effects

2016· article· en· W2471501953 on OpenAlexaboutno aff
Norman J. Stomski, Paul Morrison, Tom Meehan

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2016
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsAntipsychoticMental healthMedicineQuarter (Canadian coin)NursingPsychiatryPsychologySchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

WHAT IS KNOWN ON THE SUBJECT?: The only previous quantitative study that examined nurses' use of assessment tools to identify antipsychotic medication side effects found that about 25% of mental health nurses were using assessment tools. No previous studies have examined factors that influence the manner in which mental health nurses assess antipsychotic medication side effects. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: One-third of the respondents were not aware of any antipsychotic medication side-effect assessment tool, and only one-quarter were currently using an assessment tool. 'Service responsibility' was significantly associated with ongoing use of antipsychotic medication assessment tools, indicating that respondents with more positive attitudes to their service were more likely to continue using antipsychotic medication assessment tools. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: The low level of awareness and use of antipsychotic medication side-effect assessment tools indicates that nursing educational institutions should incorporate more detail about these tools in course content, and emphasize in particular the benefits that result from the use of these tools in clinical practice. Service processes contributed significantly to the use of antipsychotic medication assessment tools, which indicates that managers need to foster workplace cultures that promote routine use of these tools. ABSTRACT: Introduction Limited evidence suggests that only a minority of mental health nurses regularly use standardized assessment tools to assess antipsychotic medication side effects, but the factors that contribute to the non-routine use of these tools remain unknown. Aim To examine Australian mental health nurses' awareness of, and attitudes towards, side-effect assessment tools, and also identify factors the influence the use of these tools. Methods A cross-sectional survey was undertaken through distributing an online questionnaire via email to members of the Australian College of Mental Health Nurses. Completed questionnaires were received from 171 respondents. Linear regression was used to examine the relationship between the 'service responsibility' and 'personal confidence' scale scores, and awareness, previous use and ongoing use of antipsychotic medication assessment tools. Results Only one-quarter of the respondents (26.5%) were currently using an assessment tool. 'Service responsibility' was significantly associated with ongoing use of antipsychotic medication assessment tools (Β = 3.26; 95% CI 0.83-5.69). 'Personal confidence' did not influence the ongoing use of assessment tools (Β = -0.05; 95% CI -1.06-1.50). Implications for clinical practice Stakeholders can incorporate 'service responsibility' processes to foster increased use of assessment tools, which may enhance the identification antipsychotic medication side effects and improve the quality of care for service users.

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)0.006
metaresearch head score (Gemma)0.026
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.018
GPT teacher head0.387
Teacher spread0.369 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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