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Record W4224239493 · doi:10.1111/nhs.12946

Home care providers' perceptions of shared decision‐making with older clients (and their caregivers): A cross‐sectional study

2022· article· en· W4224239493 on OpenAlexafffundabout
Claudia K. Y. Lai, Paul Holyoke, Karine V. Plourde, Lily Yeung, France Légaré

Bibliographic record

VenueNursing and Health Sciences · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsInstitute of Particle PhysicsUniversité LavalCentre hospitalier de l'Université LavalOPKO Health (Canada)University of VictoriaUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsRespondentNursingPerceptionHealth careCross-sectional studyRehabilitationMedicinePsychologyFamily medicine

Abstract

fetched live from OpenAlex

Interprofessional care teams can play a key role in supporting older adults (and caregivers) in making informed health decisions, yet shared decision making is not widely practiced in home care. Based on an earlier needs assessment with older adults (and caregivers) with home care experience, we aimed to explore the perceptions of home care teams on the decisions facing their clients and their perceived involvement in shared decision making. A cross-sectional study was conducted with 614 home care providers (nurses, personal support workers, rehabilitation professionals) in three Canadian provinces (Quebec, Ontario, and Alberta). Home care providers considered the decision "to stay at home or move" as the most difficult for older adults. Those most frequently involved in decision making with older adults were family members and least involved were physicians. Although all home care providers reported high levels of shared decision-making, we detected an effect of respondent's discipline on self-perceived shared decision-making; nurses and rehabilitation professionals reported significantly higher levels of shared decision making than personal support workers. A more tailored approach is required to support shared decision making in interprofessional care teams.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0110.001
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 head0.167
GPT teacher head0.474
Teacher spread0.308 · 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.

Study designObservational
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

Citations17
Published2022
Admission routes3
Has abstractyes

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