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Record W2794068419 · doi:10.1002/nop2.123

Older people and their families’ perceptions about their experiences with interprofessional teams

2018· article· en· W2794068419 on OpenAlexafffund
Sherry Dahlke, Kim Steil, Rosalie Freund‐Heritage, Marnie Colborne, Susan Labonte, Adrian Wagg

Bibliographic record

VenueNursing Open · 2018
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsAlberta Health ServicesGlenrose Rehabilitation HospitalAlberta HealthUniversity of Alberta
FundersFaculty of Nursing, University of AlbertaUniversity of Alberta
KeywordsPerceptionContent analysisOlder peopleQualitative researchVariety (cybernetics)PsychologyNursingNaturalistic observationMedicineMedical educationGerontologySocial psychologySociology

Abstract

fetched live from OpenAlex

Aim: To examine older people and their families' perceptions about their experiences with interprofessional teams. Design: Naturalistic inquiry using qualitative descriptive methods to provide a comprehensive summary of older people and their families' experiences with interprofessional teams. Methods: Interviews were conducted with 22 people from 11 families. The families had experiences with teams in a variety of settings, such as community, residential care and hospital. Data were analysed using inductive content analysis. NiVivo was used to record preliminary codes. Analysis included comparing and contrasting families' experiences. Results: Older people and their families wanted communication about what was going on, regardless of whether the news was good, bad or unknown. They also wanted care that took the concerns of the older person into consideration. Communication was a necessary ingredient to ensuring that the older person's unique concerns were known to the interprofessional team. These percepectives were discussed in the themes of communication and patient-centred care.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.002
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.402
Teacher spread0.354 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2018
Admission routes2
Has abstractyes

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