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Interprofessional approach to fall prevention in hospital care

2024· article· en· W4400361684 on OpenAlexaboutno aff
Anna Carolina da Silva Albertini, Marina Peduzzi

Bibliographic record

VenueRevista da Escola de Enfermagem da USP · 2024
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsFall preventionThematic analysisFocus groupNursingTeamworkContext (archaeology)MedicineHealth careQualitative researchExploratory researchSuicide preventionPsychologyMedical educationPoison controlMedical emergencySociology

Abstract

fetched live from OpenAlex

OBJECTIVE: To understand the perception and experience of health professionals regarding fall prevention practices in hospital inpatient units. METHOD: This is a qualitative exploratory and descriptive case study based on the Canadian framework of interprofessional competences. Data was collected from two focus groups, with different health professionals in each group, and thematic content analysis was used. RESULTS: Five categories were drawn up which showed intense convergence between the participants of the two focus groups, within the context of fall prevention practices: communication between professionals and patients/carers for fall prevention, interprofessional communication for fall prevention, clarification of roles for fall prevention, health education about risk and fall prevention and continuing education for fall prevention. CONCLUSION: Teamwork and collaborative practice are important for achieving good results in the prevention of falls in hospital care, but for this to happen, health professionals need to acquire the necessary competences for collaborative action.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0040.002
Open science0.0010.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.034
GPT teacher head0.390
Teacher spread0.357 · 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 designObservational
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

Citations3
Published2024
Admission routes1
Has abstractyes

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