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Use of retrospective pre/post assessments in faculty development

2008· article· en· W2087537317 on OpenAlexaff
Peter J. McLeod, Yvonne Steinert, Linda Snell

Bibliographic record

VenueMedical Education · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsMcGill University
Fundersnot available
KeywordsIntervention (counseling)Context (archaeology)Medical educationPsychologyRetrospective cohort studyScale (ratio)PerceptionMedicineApplied psychologyNursingSurgery

Abstract

fetched live from OpenAlex

Context and setting Our school’s faculty development (FD) programme annually produces 6 or more FD workshops covering a range of topics. We ordinarily assess these workshops, which each attract 20–60 participants from a wide variety of disciplines, using post-workshop questionnaires. In order to enhance the value of our evaluations, we decided to assess the usefulness of retrospective pre/post assessments. Why the idea was necessary A recent review of the FD literature concluded that assessment of outcome is often poorly carried out, and that measurement of true impact is difficult. It is common practice to elicit participants’ perceptions after an intervention. However, even when combining this with pre-intervention self-assessment, the validity is often limited by the phenomenon of ‘response shift bias’. That is, participants tend to over-rate themselves before the intervention and then rate themselves more accurately afterwards when they are more familiar with the content. Retrospective pre/post self-assessments, which are completed after an intervention, offer promise of greater validity. This method allows participants to rate their perceptions of their knowledge and skills after a workshop has been completed and to simultaneously rate their perceptions of their knowledge and skills beforehand. What was done Our FD series included workshops on ‘Teaching when there is no time to teach’ (TWNTT) and ‘Designing successful workshops’ (DSW), both of which were repeated 1 year later. At the end of each workshop, participants completed a retrospective pre/post self-assessment questionnaire. Participants in the TWNTT workshop rated 15 items on a scale of 1–5, indicating their perceptions of their knowledge and skills after completing the workshop and their perceptions of how their knowledge and skills had been before the workshop. We used the same procedure in the DSW workshop, but the latter included only 5 questionnaire items. We calculated mean differences and standard deviations between pre- and post-workshop scores on the questionnaires and, using a Wilcoxon statistic, evaluated the differences in each pre/post mean item score. Evaluation of results and impact Fifty faculty members participated in 1 TWNTT workshop; 49 different faculty attended the same workshop 1 year later. The DSW workshop was attended by 33 and 40 faculty staff at the same time-points. Self-ratings on all competency items were significantly higher on the retrospective post-workshop items than on the retrospective pre-workshop items for all 4 workshops. In both pairs of workshops, the mean changes in the pre/post ratings for each item were remarkably similar. This self-assessment process consists of only 1 brief questionnaire and is minimally intrusive. It may help to avoid the response shift bias inherent in traditional pre and post self-assessments caused by pre-test over- or underestimation. The remarkable stability and robust nature of our data, and the published evidence indicating that post-workshop interviews support this type of self-assessment, lead us to conclude that retrospective pre/post assessment is a valuable measure of the impact of faculty development.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.067
GPT teacher head0.437
Teacher spread0.371 · 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

Citations19
Published2008
Admission routes1
Has abstractyes

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