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Record W2149542053 · doi:10.22230/jripe.2012v2n2a47

Survey of Interprofessional Collaboration Learning Needs and Training Interest in Health Professionals, Teachers, and Students: An Exploratory Study

2012· article· en· W2149542053 on OpenAlexaffvenue
Krista Baerg, Deborah Lake, Teresa Paslawski

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2012
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of AlbertaUniversity of Saskatchewan
Fundersnot available
KeywordsPracticumMedical educationPsychologyTraining (meteorology)Medicine

Abstract

fetched live from OpenAlex

AbstractBackground: Researchers and trainers from many professions and settings have emphasized the importance of explicit training in interprofessional collaboration (IPC), but interest in and best practice for training for IPC remains unknown.Methods and Findings: A 33-item Internet-based survey was completed by 486 practicing professionals and students from the sectors of health and education. The survey assessed experiences and knowledge of IPC as well as interest in and barriers to further training in IPC. Overall, there was agreement among respondents regarding the importance of IPC. Satisfaction with IPC was associated with higher self-ratings of knowledge and skills related to IPC. Interest in further IPC training was high, especially for one- or two-day workshops or web-based modules. Qualitative analysis of responses to an open-ended question about IPC skills and knowledge revealed seven networks of common themes that can serve as a framework for training and theory development.Conclusions: IPC training should provide knowledge about IPC models and research, leadership styles, team stages, and conflict management, while also ensuring that training applies to the workplace or practicum placement. Efforts should be made to promote awareness of the need for training in areas where trainees already feel competent.

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)0.035
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.004
Open science0.0000.000
Research integrity0.0000.004
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 head0.265
GPT teacher head0.613
Teacher spread0.348 · 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

Citations22
Published2012
Admission routes2
Has abstractyes

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