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A Qualitative Study on the Transfer of Residents Between a Nursing Home and an Emergency Department

2011· article· en· W1564552351 on OpenAlexaffabout
Rose McCloskey

Bibliographic record

VenueJournal of the American Geriatrics Society · 2011
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMedicineEmergency departmentNursingFeelingQualitative researchHealth careAcute careNursing homesWork (physics)

Abstract

fetched live from OpenAlex

Nursing home (NH) residents who have exacerbations of chronic health conditions or new illnesses must generally go the emergency department (ED) for health care, later returning to the nursing home when it is felt that they are no longer require acute care. Transfers between settings of care are referred to as transitions, and research has shown that residents are at risk of experiencing negative health outcomes during these periods. This article reports on a qualitative study of resident transfers between one NH and one ED in Canada. Data were collected using interviews, participant observation, and examination of institutional policies and standard practices. Three themes emerged from the data: (1) work of executing transfers; (2) creating and exchanging resident information; and (3) feelings of guilt but not being responsible about how residents' transfers occurred. Although completion of organization-specific forms consumed a considerable amount of practitioners' time, they contributed little to resident transfers or to the sharing of information. There is a need for integrated models of care that transcend settings and promote an understanding of the roles and responsibilities of practitioners working along the entire continuum of care.

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)0.015
metaresearch head score (Gemma)0.024
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0160.012
Scholarly communication0.0040.005
Open science0.0020.005
Research integrity0.0020.003
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 head0.130
GPT teacher head0.466
Teacher spread0.336 · 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

Citations52
Published2011
Admission routes2
Has abstractyes

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