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Record W4366525762 · doi:10.1177/20534345231169883

Older adults with complex care needs’ experiences within a hospital-to-home transition patient navigator program: A qualitative descriptive study

2023· article· en· W4366525762 on OpenAlexaffabout
Kristina M. Kokorelias, Hardeep Singh, Amanda Knoepfli, Tracey Das Gupta, Naomi Ziegler, Sander L. Hitzig

Bibliographic record

VenueInternational Journal of Care Coordination · 2023
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsThematic analysisPsychosocialQualitative researchNursingMedicineFamily medicineGerontologyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

Introduction Older adults living with complex care needs are often hospitalized due to poor community support. Recommendations for improving patient and family experiences include having a patient navigator to support patients and caregivers in hospital and community settings. This study aimed to report on older adults’ and caregivers’ experiences of receiving services from a hospital-to-home patient navigation program. Methods A qualitative descriptive study was conducted. Telephone interviews were conducted with 14 older adults with complex care needs or their family caregivers in Toronto, Ontario from 2020 to 2021. Data were analyzed using inductive thematic analysis. Results Four themes were identified: (1) Initial Hesitancy; (2) Meeting Evolving Needs; (3) Unexpected Benefits of Patient Navigation; and (4) The Value of Patient Navigation in Sustaining Aging-in-Place. Participants viewed all their interactions with the navigator as positive, and perceived navigators improved their quality of care. Discussion These study findings suggest that patient navigators may be well-positioned to address gaps in services around psychosocial support and care coordination while also encouraging self-management in older adults with complex care needs.

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)0.000
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.059
GPT teacher head0.421
Teacher spread0.362 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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