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Record W4200572916 · doi:10.1177/07334648211059056

Designing the Ideal Patient Navigation Program for Older Adults with Complex Needs: A Qualitative Exploration of the Preferences of Key Informants

2021· article· en· W4200572916 on OpenAlexaff
Kristina M. Kokorelias, Tracey DasGupta, Sander L. Hitzig

Bibliographic record

VenueJournal of Applied Gerontology · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsThematic analysisKey (lock)Ideal (ethics)PsychologyHealth careNursingQualitative researchMedical educationMedicineComputer science

Abstract

fetched live from OpenAlex

Navigating the healthcare system is complex. Many older adults and their family members report sub-optimal outcomes when transitioning from hospital to home. Patient navigation has been introduced as a model of care to help improve hospital to home transitions and to better integrate care across care environments. There are no best-practice guidelines for designing a patient navigation program for older adults with complex needs. This qualitative descriptive study interviewed 38 healthcare professionals to determine key characteristics of the "ideal" patient navigator program. Thematic analysis revealed four themes describing key components of an ideal patient navigator program for older adults with complex needs: (1) Easy accessibility and open communication amongst staff; (2) flexible eligibility requirements; (3) characteristics of the patient navigator; and (4) appropriate program size and duration. We suggest directions for future research, program design, and implementation considers to improve patient navigation for older adults and their family caregivers.

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)0.001
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 score0.030
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.284
GPT teacher head0.453
Teacher spread0.169 · 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

Citations23
Published2021
Admission routes1
Has abstractyes

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