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Record W2014676380 · doi:10.1080/13576280500042606

Influence of an Interprofessional HIV/AIDS Education Program on Role Perception, Attitudes and Teamwork Skills of Undergraduate Health Sciences Students

2005· article· en· W2014676380 on OpenAlexaff
Vernon Curran, J Gerry Mugford, Rebecca MT Law, Sandra MacDonald

Bibliographic record

VenueEducation for Health · 2005
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTeamworkInterprofessional educationMedical educationPharmacyPerceptionHealth careMedicineNursingPsychology

Abstract

fetched live from OpenAlex

PURPOSE: An evaluation study of an undergraduate HIV/AIDS interprofessional education program for medical, nursing and pharmacy students was undertaken to assess changes in role perception, attitudes towards collaboration, self-reported teamwork skills and satisfaction with a shared learning experience. METHODS: A combined one group pretest-posttest and time-series study design was used. Several survey instruments and observation checklists were completed by students and tutors before, during and after the educational program. RESULTS: Students reported greater awareness of roles and the continuous exposure to interprofessional learning led to improved attitudes towards teamwork. Standardized patients were effective in fostering an experience of realism and motivating collaboration between students. CONCLUSION: A problem-based learning approach combined with standardized patients was effective in enhancing HIV/AIDS interprofessional role perception, enhancing attitudes towards collaboration and interprofessional approaches to HIV/AIDS care and fostering confidence in teamwork skills among pre-licensure health sciences students.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.505
Teacher spread0.488 · 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 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

Citations50
Published2005
Admission routes1
Has abstractyes

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