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Record W2140391106 · doi:10.3109/0142159x.2012.746447

DOPS assessment: A study to evaluate the experience and opinions of trainees and assessors

2013· article· en· W2140391106 on OpenAlexaff
Natish Bindal, Helen Goodyear, Taruna Bindal, David Wall

Bibliographic record

VenueMedical Teacher · 2013
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsMedical educationMedicinePost hoc

Abstract

fetched live from OpenAlex

BACKGROUND: Workplace based assessments (WBAs) have been part of UK training for the last 3 years. Carrying out procedures efficiently and safely is of paramount importance in anaesthesia. AIMS: To explore opinions and experiences of Direct Observation of Procedural Skills (DOPS) assessments in a regional anaesthetic training programme. METHODS: 19 and 20-item questionnaires were distributed to trainees and consultants respectively. RESULTS: Questionnaire response rate was 76% (90/119) for trainees and 65% (129/199) for consultants. 43% of consultants and 33% of trainees were not trained in DOPS use. Assessments were usually not planned. 50% were ad hoc and the remainder mainly retrospective. Time spent on assessment was short with DOPS and feedback achieved in ≤15 minutes in the majority of cases with lack of suggestions for further improvement. Both trainees and consultants felt that DOPS was not a helpful learning tool (p = 0.001) or a reflection of trainee competency. CONCLUSIONS: DOPS assessments are currently not valued as an educational tool. Training is essential in use of this WBA tool which needs to be planned and sufficient time allocated so as to address current negative attitudes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.065
GPT teacher head0.420
Teacher spread0.354 · 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

Citations59
Published2013
Admission routes1
Has abstractyes

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