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Record W2160621846 · doi:10.1080/01421590801949958

Planning and implementing a collaborative clinical placement for medical, nursing and allied health students: A qualitative study

2008· article· en· W2160621846 on OpenAlexaff
Scott Reeves

Bibliographic record

VenueMedical Teacher · 2008
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsMedical educationNursingQualitative researchMedicineSalientPsychologySociologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Clinical placements have been traditionally offered on a profession specific basis, and as a result, we have a good understanding of salient issues related to their development and delivery. We know less about the planning and implementation of collaborative clinical placements. AIMS: This paper presents key findings from a qualitative study that explored the collaborative processes connected to an interprofessional planning group who created and implemented a clinical placement for medical, nursing and allied health students. METHODS: An ethnographic approach was employed to explore the successes and challenges connected with the planning group's interprofessional work. Interviews, observations and documents were gathered over two years to obtain a comprehensive understanding of this placement. RESULTS: The study found that while the planning group achieved a number of successes in their work including the implementation of a well-received pilot placement, their enthusiasm for the placement created a number of challenges. In particular, it resulted in them neglecting their roles, responsibilities and collaborative group processes, which created difficulties in their ability to work together. In addition, a turnover of members, changes in management and a hospital reorganization inhibited the group's collaborative work. CONCLUSIONS: Collaboration around the planning and implementation of interprofessional placements is a complex venture. In striving for success in this work, planning groups need to focus their attention on both internal group-based factors as well external organizational factors.

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)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.174
GPT teacher head0.659
Teacher spread0.485 · 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 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

Citations17
Published2008
Admission routes1
Has abstractyes

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