MétaCan
Menu
Back to cohort
Record W2946753941 · doi:10.3138/ptc-2018-43.e

Students’ Perspectives on Their Experience in Clinical Placements: Using a Modified Delphi Methodology to Engage Physiotherapy Stakeholders in Revising the National Form

2019· article· en· W2946753941 on OpenAlexaffvenueabout
Brenda Mori, Martine Quesnel, Sarah Wojkowski

Bibliographic record

VenuePhysiotherapy Canada · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsMcMaster UniversityUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsDelphi methodPhysical therapyDelphiMedical educationMedicinePhysical medicine and rehabilitationPsychologyComputer scienceNursingArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose: We developed an evidence-informed Student Evaluation of the Clinical Placement form. This form gives students the opportunity to share their feedback and perceptions of their clinical placement experiences and provides meaningful data to all stakeholders. Method: We used a modified Delphi process to engage a sample of national stakeholders: physiotherapy clinical education leads of academic departments, centre coordinators of clinical education, clinical instructors, and students. An expert consultant panel, in addition to the investigators, reviewed the responses from each round and helped develop the questionnaire for the subsequent round and finalize the evaluation form. Results: The response rate was 65.3% (47 of 72) for Round 1, 76.6% (36 of 47) for Round 2, and 100% (36 of 36) for Round 3. After three rounds of questionnaires, 89% of participants thought that the evaluation form met their needs. Conclusions: We developed a revised Student Evaluation of the Clinical Placement form that is informed by the literature and meaningful to all stakeholders. This form is being implemented in physiotherapy university programmes across Canada to enable students to share their experiences at clinical sites.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.510
GPT teacher head0.582
Teacher spread0.072 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2019
Admission routes3
Has abstractyes

Explore more

Same venuePhysiotherapy CanadaSame topicDelphi Technique in ResearchFrench-language works237,207