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Record W4212806856 · doi:10.12927/hcq.2022.26710

Mixed-Methods Evaluation of a Patient Behaviour Risk Screening, Communication and Care Planning Intervention for Hospital Settings

2022· article· en· W4212806856 on OpenAlexaffvenue
Marija Corovic, Karen Spithoff, Jon-David Schwalm, Denise Johnson, Susan Fuciarelli, Erika Caspersen, Tony DeBono, Melissa Brouwers, Elaine Principi

Bibliographic record

VenueHealthcare Quarterly · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsRoyal Ottawa Mental Health CentreHamilton Health SciencesPopulation Health Research InstituteMcMaster University
Fundersnot available
KeywordsAuditPatient safetyPsychological interventionNursingIntervention (counseling)Focus groupMedicineHealth careMedical emergencyBusiness

Abstract

fetched live from OpenAlex

Workplace violence is a common safety concern for hospital staff. The Behaviour Safety Risk Communication and Care Planning program identifies, manages and cares for patients at risk of exhibiting unsafe behaviours. This paper reports on a mixed-methods evaluation consisting of staff surveys, focus groups and open forums, screening audits, patient interviews and assessment of effectiveness measures at five hospital sites. Staff perceptions about safety risk imposed by a patient's behaviour significantly improved after this program was implemented. Opportunities exist to improve staff adherence to screening processes and communication with patients. This study provides insight for teams implementing similar interventions.

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)0.033
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 score0.033
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.412
Teacher spread0.371 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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