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Record W4402556220 · doi:10.1080/02602938.2024.2400349

Feedback practices in clinical placement: how students come to understand how they are progressing

2024· article· en· W4402556220 on OpenAlexaff
Kelli Nicola‐Richmond, Nikki Lyons, Natalie Ward, Sally Logan, Rola Ajjawi

Bibliographic record

VenueAssessment & Evaluation in Higher Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyMathematics educationPedagogyHigher educationAdvanced PlacementMedical educationMedicinePolitical science

Abstract

fetched live from OpenAlex

Health clinicians are required to make use of feedback to form effective judgements and deliver efficacious health care. In health education, clinical placements provide an opportunity for students to translate knowledge to practise and develop expertise. During placements students are expected to engage with and act on feedback in order to improve and demonstrate competence. Increasingly, in the assessment literature, students’ ability to judge the quality of their own performance (known as evaluative judgement) is considered key to placement success. Despite this, there is limited empirical research examining student feedback practices and their influence on evaluative judgement in the placement environment. This study sought to examine feedback practices in clinical placement and their contribution to the development of evaluative judgement. Three participant groups, occupational therapy students, placement supervisors, and university placement support staff took part in semi-structured interviews. Data were analysed using document and thematic analyses. Findings suggest students use a range of active feedback practices during placement, seeking feedback from multiple sources in different forms. Feedback practices appear pivotal in developing evaluative judgement but this often requires patching together of disparate, complex and competing information. Placement supervisors need to scaffold feedback so that students can develop evaluative judgement.

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.288
GPT teacher head0.573
Teacher spread0.285 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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