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Programme evaluation using student self‐assessments

2010· article· en· W2062418347 on OpenAlexaff
Krista Trinder, Marcel D’Eon

Bibliographic record

VenueMedical Education · 2010
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCLARITYMedical educationStrengths and weaknessesClass (philosophy)Self-assessmentSet (abstract data type)Scale (ratio)Educational measurementFocus groupPsychologyMedicineProgram evaluationCurriculumComputer sciencePedagogy

Abstract

fetched live from OpenAlex

Our College of Medicine has developed a set of goals and objectives for its undergraduate programme covering a number of roles. To help identify the strengths and weaknesses of the overall programme, a tool reflecting these objectives has been developed based on grouped student self-assessments. Grouped self-assessment data have been previously validated for programme evaluation purposes, with self-assessments corresponding to third-party evaluations. Evaluating the efficacy of an undergraduate programme is complex, especially when specific strengths and weaknesses are to be identified. Conventional sources of data, such as residency placements, failure rates, board and licensing examinations and course evaluations, are often non-specific, do not address non-Medical Expert roles, focus on process and are subjective. The current project specifically addresses the college’s goals and objectives, which include promoting roles other than that of Medical Expert. A total of 64 objectives worded in the form of questions were administered to students in the class of 2010 immediately prior to the start of their clerkship and to the class of 2009 upon completing clerkship. Evaluations were completed anonymously online. This self-assessment was completed by 49 pre-clerkship and 27 post-clerkship students, reflecting response rates of 82% and 47%, respectively. Prior to distribution, the items were reviewed for clarity and pilot-tested by clinical clerks. When completing the self-assessment, students were asked to rate the extent to which they were currently able to meet the requirements outlined by each item on a scale of 1 (Not at all) to 10 (Very much) and the extent to which they had achieved each objective on their first day of medical school. Prior analysis from the class of 2010 revealed that students perceived that their abilities increased significantly from their first day of medical school for nearly all items. Independent-samples t-tests were conducted to measure statistically significant changes. Results indicate that post-clerkship medical students rated their increase in abilities higher than pre-clerkship students on the general objective (t[47] = − 3.93, P = 0.000, d = 1.13) and the categories of doctor as Medical Expert (t[47] = − 2.64, P = 0.011, d = 0.77), Communicator (t[48] = − 3.35, P = 0.002, d = 0.96), Health Advocate (t[50] = − 4.02, P = 0.000, d = 1.11), Collaborator (t[50] = − 2.71, P = 0.009, d = 0.75) and Resource Manager (t[48] = − 3.40, P = 0.001, d = 0.96). Thus, perceived skill in these areas appears to increase after completing clerkship, which demonstrates known-groups validity for the instrument. Overall, both pre- and post-clerkship students reported large gains in their perceived ability for objectives reflecting medical expertise, but smaller gains were reported for items reflecting interpersonal skills. All categories were found to be internally consistent (Cronbach’s α > 0.70). In summary, results indicate that post-clerkship medical students rate their abilities more highly than pre-clerkship students. The class of 2010 will complete this self-assessment near graduation and their pre- and post-clerkship responses will be compared to measure changes in perceived ability over time.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.038
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.065
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.041
GPT teacher head0.507
Teacher spread0.466 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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