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Fall and fall-related injury studies among older aboriginal people in Australia, Canada, New Zealand and the USA: a systematic review

2012· review· en· W1979111059 on OpenAlexaffabout
V Scott, Scott Metcalfe, Yara Yassin

Bibliographic record

VenueInjury Prevention · 2012
Typereview
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsSpinal Cord Injury BCUniversity of British Columbia
Fundersnot available
KeywordsPoison controlInjury preventionSeriousnessSuicide preventionHuman factors and ergonomicsTerminologyOccupational safety and healthMedicineGrey literatureGerontologyInclusion (mineral)Fall preventionScope (computer science)MEDLINEMedical emergencyPsychologyPolitical science

Abstract

fetched live from OpenAlex

Background Little is known about the scope or nature of falls or fall prevention among older Aboriginal/Indigenous people. Aims/Objectives/Purpose To identify peer-reviewed literature with epidemiology and prevention evidence on the topic of falls and fall-related injury among older Aboriginal people in Australia, Canada, New Zealand and the United States. Methods A key word search of relevant databases was conducted using combinations of terminology for accidental falls and a comprehensive list of terms for Aboriginal status specific to each country. Each study was independently reviewed by two reviewers against the inclusion criteria with discrepancies determined by a third. Results/Outcome 34 publications met the review criteria; United States=14; Australia=8; Canada=7; New Zealand=3; international=2. Most are morbidity (14)/mortality (6) reports or reviews (5), with very little on fall prevention. Significance/Contribution to the Field The findings bring greater awareness to the seriousness of the issue and point to areas of urgent need for future research.

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)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.927
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0130.017
Science and technology studies0.0010.001
Scholarly communication0.0020.002
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 head0.046
GPT teacher head0.403
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 designSystematic review
Domainnot available
GenreReview

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
Published2012
Admission routes2
Has abstractyes

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