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Record W3186280379 · doi:10.1177/23743735211034047

Improving Communications With Patients and Families in Geriatric Care. The How, When, and What

2021· article· en· W3186280379 on OpenAlexaffabout
Shirley Huang, Alden L.R. Morgan, Vanessa Peck, Lara Khoury

Bibliographic record

VenueJournal of Patient Experience · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsTelephone interviewScale (ratio)Health careFamily caregiversFamily medicineNursingPsychologyMedicineTelephone surveyHealth communication

Abstract

fetched live from OpenAlex

There has been little published literature examining the unique communication challenges older adults pose for health care providers. Using an explanatory mixed-methods design, this study explored patients' and their family/caregivers' experiences communicating with health care providers on a Canadian tertiary care, inpatient Geriatric unit between March and September 2018. In part 1, the modified patient-health care provider communication scale was used and responses scored using a 5-point scale. In part 2, one-on-one telephone interviews were conducted and responses transcribed, coded, and thematically analyzed. Thirteen patients and 7 family/caregivers completed part 1. Both groups scored items pertaining to adequacy of information sharing and involvement in decision-making in the lowest 25th percentile. Two patients and 4 family/caregivers participated in telephone interviews in part 2. Interview transcript analysis resulted in key themes that fit into the "How, When, and What" framework outlining the aspects of communication most important to the participants. Patients and family/caregivers identified strategic use of written information and predischarge family meetings as potentially valuable tools to improve communication and shared decision-making.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.076
GPT teacher head0.363
Teacher spread0.287 · 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 routes2
Has abstractyes

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