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Record W2803178870 · doi:10.3138/ptc.2016-102.e

Why So Stressed? A Descriptive Thematic Analysis of Physical Therapy Students' Descriptions of Causes of Anxiety during Objective Structured Clinical Exams

2018· article· en· W2803178870 on OpenAlexaffvenueabout
Nancy R. Zhang, David M. Walton

Bibliographic record

VenuePhysiotherapy Canada · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsWestern University
Fundersnot available
KeywordsAnxietyThematic analysisPsychologyDescriptive statisticsCompetence (human resources)Medical educationDescriptive researchClinical psychologyMedicineSocial psychologyQualitative researchPsychiatry

Abstract

fetched live from OpenAlex

Purpose: The purpose of this study was to collect and conduct a descriptive content analysis of the primary triggers of practical exam anxiety in Master of Physical Therapy (MPT) students in a Canadian university programme. Method: First and second-year MPT students were invited to reflect upon their top 5 sources or triggers of OSCE exam anxiety, collected in written format during a low-stress, low-examination period of their programme. All participants had participated in at least 3 OSCEs before providing data. The emergent themes were member-checked with 10 of the original participants to improve trustworthiness of the results. Results: 56 of a possible 105 students provided 224 triggers of OSCE anxiety. Thematic content analysis revealed 6 emergent meta-themes that adequately captured all triggers. They were: social performance anxiety, fear of lacking competence, overvaluing the outcome, fear of the unknown, impaired personal health/coping resources, and operational/procedural influences. These meta-themes were endorsed by the participant sub-group. Conclusions: OSCEs are common forms of evaluation in MPT training programmes, but are also highly anxiogenic. The first step toward mitigating exam anxiety, thereby ensuring exam performance is less confounded by anxiety, is to identify the common triggers. Confidence in results will be strengthened by replication in other programmes.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.369
Teacher spread0.347 · 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 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

Citations27
Published2018
Admission routes3
Has abstractyes

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