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Record W4386284087 · doi:10.32920/24050706.v1

Nurses' experiences with patient safety incidents in long-term care

2023· preprint· en· W4386284087 on OpenAlexaffabout
Nicole Serre

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsToronto Metropolitan UniversityLaurentian University
Fundersnot available
KeywordsPatient safetyNursingSafety cultureQualitative researchPsychologyPopulationMedicineHealth careEnvironmental healthManagementSociology

Abstract

fetched live from OpenAlex

As the needs of the aging Canadian population continue to rise and increase the demands for resources in long-term care (LTC), emphasis on ensuring resident safety is required. The purpose of this qualitative descriptive study was to describe nurses’ experiences with patient safety incidents (PSIs) involving LTC residents. This research study was underpinned by the Canadian Incident Analysis Framework (Canadian Patient Safety Institute [CPSI], 2012) and psychological safety (Edmondson, 2004). Two registered nurses and seven registered practical nurses working in LTC homes participated in the research interviews. Overall, three main categories emerged from the content analysis, including (a) commitment to resident safety, (b) workplace culture and (c) emotional reaction. Providing nurses with an opportunity to share their PSI experiences can provide management of what influences resident safety at the frontline. Study findings could inform the development of workplace learning initiatives to support PSI identification and management.

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)0.007
metaresearch head score (Gemma)0.022
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 score0.015
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.005
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0010.003
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 head0.091
GPT teacher head0.505
Teacher spread0.414 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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