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Record W2096009950 · doi:10.1177/0898264311432312

I Want to Move, But Cannot

2012· article· en· W2096009950 on OpenAlexaffabout
Lisa Strohschein

Bibliographic record

VenueJournal of Aging and Health · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOddsSocioeconomic statusNational Health Interview SurveyGerontologyPopulationSample (material)DistressPsychologyDemographyLogistic regressionMedicineEnvironmental healthSociology

Abstract

fetched live from OpenAlex

OBJECTIVES: The purpose of this study was to investigate characteristics of seniors in the Canadian population who are involuntary stayers and to assess associations with health. METHOD: Data come from the 1994 Canadian National Population Health Survey, with the sample restricted to those 65 and older (N = 2,551). RESULTS: Nearly 1 in 10 seniors identified as an involuntary stayer. Seniors with few socioeconomic resources, poor health, greater need for assistance, and low social involvement were more likely to identify as an involuntary stayer. Furthermore, seniors who were involuntary stayers report significantly more distress and greater odds of low self-rated health than other seniors. DISCUSSION: This study brings into visibility an understudied segment of the elderly population: seniors who are unable to move from their present location despite their desire to do so. Further research and policy responses assisting seniors to age in a setting of their own choosing are needed.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.454

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0170.008

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 head0.050
GPT teacher head0.379
Teacher spread0.329 · 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 designQualitative
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

Citations30
Published2012
Admission routes2
Has abstractyes

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