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Record W2147972149 · doi:10.1177/0163278704267036

Evaluating Institutionalization by Comparing the Use of Health Services before and after Admission to a Long-Term-Care Facility

2004· review· en· W2147972149 on OpenAlexaffabout
Donna M. Wilson, Corrine D. Truman

Bibliographic record

VenueEvaluation & the Health Professions · 2004
Typereview
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsAlberta HealthUniversity of Alberta
Fundersnot available
KeywordsLong-term careMedicineDisadvantagedInstitutionalisationAmbulatory careHospital admissionHealth carePopulationType of serviceGerontologyFamily medicineNursingEnvironmental healthBusinessService (business)Psychiatry

Abstract

fetched live from OpenAlex

Despite concern over increased health services utilization with population aging, few studies describe health services utilization by long-term-care (LTC) residents. An investigation was designed to compare health services use before and after LTC admission. Comprehensive 1988 to 1999 data for all LTC residents (N = 47,510) in Alberta, Canada, were obtained. Utilization comparisons involved equal pre/post timeframes. Only non-hospital physician services increased post-LTC admission. Home care was not provided after admission (51% had been recipients). Hospital and ambulatory services use declined, with these patterns stable for 5 years pre- and post-LTC admission. When hospital or ambulatory care was sought by LTC residents, they were not disadvantaged in the type or scope of care as compared to the care received prior to LTC admission. These findings should raise interest in the services provided by LTC facilities and the outcomes of long-term, facility-based care. LTC services could be beneficial for people with advanced age and dependency.

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)0.004
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.008
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.360
GPT teacher head0.563
Teacher spread0.203 · 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
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

Citations14
Published2004
Admission routes2
Has abstractyes

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