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Record W3172814260 · doi:10.1155/2021/5362197

Development of the Home Fall Hazard Checklist

2021· article· en· W3172814260 on OpenAlexafffund
Christina Ziebart, Neha Dewan, Joshua R. Tuazon, Joy C. MacDermid

Bibliographic record

VenueRehabilitation Research and Practice · 2021
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsSt. Joseph's HospitalUniversity of TorontoLakehead UniversityWestern University
FundersCanadian Institutes of Health Research
KeywordsMedicineChecklistHazard

Abstract

fetched live from OpenAlex

Objective. Home hazard assessment is particularly important following a fracture as a means of preventing subsequent fractures. The purpose of this study was to evaluate current checklists and evidence on home hazard to develop a usable self-administered checklist that could be used by adults to assess home hazards. Design. Review and observational, prospective study. Setting. Community dwelling. Participants. Nine adults (4 men, 5 women) were asked to review the checklist and provide feedback on whether items were relevant, comprehensive, and easy to understand. Intervention. A search for literature examining the causes of falls that focused on home hazards or behaviours was conducted, and causes were extracted. Using the combined list of home hazards, a draft checklist was created. The participants were asked to pilot the checklist through their home. Primary and Secondary Outcome. An initial iteration of the checklist was modified to reduce redundancy (by grouping certain items together), improve usability (by adding a “not applicable category”), and improve readability (by removing double-barrelled questions or rewriting certain items). Results. This process resulted in 74 items in 10 areas. On average, it took 10 minutes for the participants to complete the home walk-through while filling out the checklist. Conclusion. The fall hazard-home checklist is a new checklist designed to identify home fall hazards with the intended use of being either administered by self-report through memory or supported by a walk-about, and that could potentially be completed by a patient who has incurred a fall, fracture, a family member, or caregiver. Given the expense of home hazard assessments that involve a home visit, the validity of this method of detection warrants further investigation.

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)0.006
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.089
GPT teacher head0.478
Teacher spread0.389 · 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.

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

Citations7
Published2021
Admission routes2
Has abstractyes

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