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Record W4309188638 · doi:10.1177/25160435221135115

Investigating areas for improvement in the transition from hospital-to-home for frail older adults: A mixed methods study

2022· article· en· W4309188638 on OpenAlexafffund
Leanne Skerry, Emily Kervin, Natasha Hanson, Pamela Jarrett, Rose McCloskey

Bibliographic record

VenueJournal of Patient Safety and Risk Management · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of New BrunswickSaint John Regional HospitalDalhousie UniversityHorizon Health Network
FundersCanadian Frailty NetworkFondation de la recherche en santé du Nouveau-Brunswick
KeywordsTransitional careThematic analysisProcess (computing)Data collectionNursingMedicineGerontologyProcess managementQualitative researchHealth careBusinessComputer scienceSociologyPolitical science

Abstract

fetched live from OpenAlex

Background The planning and execution of discharge plans to successfully transition frail older adults from hospital-to-home can be a complicated endeavour. Objective To identify areas for improvement in the transitional process of frail older adults who were discharged from hospital based, geriatric units to their homes in the community. Method A prospective multi-phased mixed methods design was used, and cross-case thematic analysis of Phase 2 data were triangulated with Phase 1 findings. Results Thematic analysis findings indicated several related areas of importance within the transitional process: 1) Coordination of discharge; 2) Transition-to-home planning; 3) Home and community care; 4) Following of recommendations; and, 5) Medical follow-up. Conclusions Strengthening communication between stakeholders, as well as the implementation of harmonized policies and guidelines are needed to facilitate more consistent care delivery and provide patients and families with information on what to expect during the transitional process.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.549
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.350
Teacher spread0.333 · 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 teacher head, 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

Citations2
Published2022
Admission routes2
Has abstractyes

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