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Record W4311134301 · doi:10.1177/07334648221144999

Understanding the Goals of Older Adults with Complex Care Needs, Their Family Caregivers and Their Care Providers Enrolled in a Patient Navigation Program

2022· article· en· W4311134301 on OpenAlexaff
Kristina M. Kokorelias, Hardeep Singh, Stephanie Posa, Sander L. Hitzig

Bibliographic record

VenueJournal of Applied Gerontology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoSunnybrook HospitalHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsNursingFamily caregiversHealth careMedicinePsychologyFamily medicine

Abstract

fetched live from OpenAlex

Patient navigation models of care are being used to support hospital-to-home transitions. The present paper aimed to explore the goals important to older adults, their caregivers, and care providers as they transition from hospital-to-home and how, if at all, patient navigation can enable goals of care. Data were comprised of 94 interviews with 16 older adults, 5 family caregivers and 48 healthcare providers. Data were analyzed thematically and similarities and differences in goals were identified. Shared goals included having someone to count on and easy access to services. Older adults expressed the goal of independence, whereas family caregivers and healthcare providers noted safety. Caregivers noted goals of developing skills. While patient navigation was viewed as meeting goals of care by participants, future research is required to determine how patient navigation can support goal setting as a standard clinical practice and the long-term patient outcomes of meeting goals.

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 score0.026
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.317
Teacher spread0.269 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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