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Record W2090384007 · doi:10.1177/10105395060180010901

Frequency and Nature of Falls among Older Women in India

2006· article· en· W2090384007 on OpenAlexaff
Shanthi Johnson

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2006
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsAcadia UniversityWestern University
Fundersnot available
KeywordsFalling (accident)MedicineContext (archaeology)Fall preventionInjury preventionSuicide preventionOccupational safety and healthGerontologyPoison controlPublic healthDemographyEnvironmental healthGeographyNursing

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the frequency and nature of falls and fall-related injuries among older women in the state of Kerala, India. The study involved 82 community living and 63 institutionalized women aged 60 years or older in Trivandrum, Kerala, India. Demographic data and falls profile were collected through the use of a field survey. A significantly lower percentage (45%) of community dwelling participants suffered a fall in the previous year, compared to 64% of those in the Long Term Care (LTC) settings (p < .05). Overall, of those who fell, 74% reported an injury (e.g., cuts and bruises, fractures) as a result of the fall, and 48% of older adults in the community and 70% in the LTC setting required medical treatment as a result of the falls. Falling is emerging as a significant public health problem facing older women in the state of Kerala. Fall prevention strategies to address falls should be explored and implemented within the Indian context.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.328
Teacher spread0.312 · 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

Citations64
Published2006
Admission routes1
Has abstractyes

Explore more

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