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Record W2343562324 · doi:10.1186/s12913-016-1382-x

Informational role self-efficacy: a validation in interprofessional collaboration contexts involving healthcare service and project teams

2016· article· en· W2343562324 on OpenAlexafffund
François Chiocchio, Paule Lebel, Jean-Nicolas Dubé

Bibliographic record

VenueBMC Health Services Research · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsCentre intégré universitaire de santé et de services sociaux de la Mauricie-et-du-Centre-du-QuébecUniversité de MontréalWilfrid Laurier UniversityUniversity of OttawaMontfort Hospital
FundersUniversité de Montréal
KeywordsNursing researchHealth informaticsHealth administrationMedicineHealth careNursingHealthcare serviceHealth services researchPublic healthService (business)Medical education

Abstract

fetched live from OpenAlex

BACKGROUND: Healthcare professionals perform knowledge-intensive work in very specialized disciplines. Across the professional divide, collaboration becomes increasingly difficult. For effective teamwork and collaboration to occur, it is considered necessary for individuals to believe in their ability to draw on their expertise and provide what others need to perform their job well. To date, however, no instruments exist to measure such a construct. METHODS: A two-study design is used to test the psychometric properties, factor structure and incremental validity of a five-item questionnaire measuring informational role self-efficacy. RESULTS: Based on parallel analysis and exploratory factor analysis, Study 1 shows a robust and reliable one-dimensional construct. Study 2 cross-validates this factor structure using confirmatory factor analysis. Study 2 also shows that informational role self-efficacy predicts proactive teamwork behaviors over and above goal similarity, interdependence, coordination and intra-team trust. CONCLUSIONS: The instrument can be used in research to assess an individual's capability beliefs in communicating his/her informational characteristics that are pertinent to the task performance of others. The construct is also shown to have value in team-building exercises.

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)0.005
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 score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.050
GPT teacher head0.504
Teacher spread0.454 · 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

Citations41
Published2016
Admission routes2
Has abstractyes

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