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Record W2076779284 · doi:10.1017/s071498081400021x

Analyzing the Interprofessional Working of a Home-Based Primary Care Team

2014· article· fr· W2076779284 on OpenAlexaff
Tracy Smith‐Carrier, Sheila M. Neysmith

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2014
Typearticle
Languagefr
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of TorontoThe King's UniversityWestern University
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Increasingly, interprofessional teams are responsible for providing integrated health care services. Effective teams, however, are not the result of chance but require careful planning and ongoing attention to team processes. Based on a case study involving interviews, participant observation, and a survey, we identified key attributes for effective interprofessional working (IPW) within a home-based primary care (HBPC) setting. Recognizing the importance of a theoretical model that reflects the multidimensional nature of team effectiveness research, we employed the integrated team effectiveness model to analyze our findings. The results indicated that a shared vision, common goals, respect, and trust among team members – as well as processes for ongoing communication, effective leadership, and mechanisms for conflict resolution – are vital in the development of a high-functioning IPW team. The ambiguity and uncertainty surrounding the context of service provision (clients' homes), as well the negotiation of external relationships in the HBPC field, require further investigation.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.287
Teacher spread0.273 · 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 designNot applicable
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

Citations29
Published2014
Admission routes1
Has abstractyes

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