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Assessing falls risk in older adult mental health patients: A Western Australian review

2012· article· en· W2163531684 on OpenAlexaff
Karen Heslop, Dianne Wynaden, Kirsten Bramanis, Claire Connolly, Trevor Gee, Rachel Griffith, Omar Al Omari

Bibliographic record

VenueInternational Journal of Mental Health Nursing · 2012
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsMedicineMental healthMental illnessOccupational safety and healthSuicide preventionInjury preventionRisk assessmentPoison controlPopulationMEDLINEHealth careHuman factors and ergonomicsGerontologyPsychiatryMedical emergencyEnvironmental health

Abstract

fetched live from OpenAlex

Falls are a common and costly complication of hospitalization, particularly in older adult populations. This paper presents the results of a review of 139 falls at two older adult mental health services in Western Australia, Australia, over a 12-month period. Data were collected from the hospital incident report management system and from case file reviews of patients who sustained a fall during hospitalization. The results demonstrated that the use of different risk assessment and falls management tools led to variations in practice, policies, and management strategies. The review identified mental health-specific falls risk factors that place older people with a mental illness at risk when admitted to the acute mental health setting. With the expansion of community mental health care, many older people with a mental illness are now cared for in a variety of health-care settings. In assessing falls risk and implementing falls-prevention strategies, it is important for clinicians to recognize this group as an ambulant population with a fluctuating course of illness. They have related risks that require specialized falls assessment 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.469
Teacher spread0.427 · 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 teacher head, 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

Citations22
Published2012
Admission routes1
Has abstractyes

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