MétaCan
Menu
Back to cohort

A Faculty Development Workshop on “Developing Successful Workshops”

2000· article· en· W2068961804 on OpenAlexaffabout
Yvonne Steinert, Louise Nasmith, Norma Daigle

Bibliographic record

VenueAcademic Medicine · 2000
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsSession (web analytics)Medical educationPlan (archaeology)PsychologyComputer scienceMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

Objective: Although workshops are a common faculty development format, efforts to improve the design and delivery of this teaching format have not been described. In 1998, the Department of Family Medicine at McGill University sponsored a three-day workshop, “Developing Successful Workshops,” to assist teachers in planning, conducting and evaluating them. Description: This workshop was designed to give participants a framework for developing successful workshops and to take them through each of the planning steps.1 On the first day, workshop modules consisted of defining participant needs, setting appropriate objectives, matching content to objectives, and matching teaching methods to content. On the second day, participants had an opportunity to apply the steps discussed on the first day to a workshop they were planning to conduct in their own setting, and to review strategies for evaluating workshops. They worked in pairs to design (or refine) their workshop content, and then presented their plan to the larger group for feedback and discussion. The last day of the workshop emphasized facilitation skills for both interactive large-group presentations and small-group discussions, and each participant was asked to present a part of his or her own workshop to the group. Each workshop module was introduced by a brief plenary session that summarized the key issues for discussion and was supplemented by a detailed handout designed to guide workshop planning. However, most of the activities took place in small groups. Discussion: The immediate post-workshop evaluations indicated that all of the participants rated the workshop “very useful.” The participants valued the systematic approach to workshop planning, the checklist provided, the hands-on experience, and the opportunity to work on one of their own workshops, with feedback from their peers. All of the sessions were rated highly (i.e., “very useful”), with the exception of the module on evaluating workshops. Six months after the workshop was held, a follow-up questionnaire was sent to all of the participants. Sixteen of thej 18 participants responded to this questionnaire; and of these, 11 reported that they had conducted the workshops they had worked on, and three had given different workshops. The participants' workshops had varied from two hours to a full day and had been given to health care providers and patients at local and national meetings. Topics addressed included community-oriented primary care, teaching evidence-based medicine, research design, and stress management. The participants continued to rate the McGill workshop very helpful (with an overall rating of 4.6 on a five-point scale) and felt that the most useful sessions were matching teaching methods to content, conducting interactive large-group presentations, setting appropriate objectives, and matching content to objectives. In retrospect, they particularly valued the structured framework provided during the workshop, the emphasis placed on careful planning, and the opportunity to see a workshop in action. The results of this follow-up evaluation confirmed the usefulness of a faculty development workshop on developing workshops, and demonstrated that a structured approach to the design and delivery of workshops can help to improve teaching and learning.

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)0.026
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.031
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0060.003
Scholarly communication0.0050.005
Open science0.0050.013
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0310.012

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 head0.277
GPT teacher head0.538
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueAcademic MedicineSame topicHealth Sciences Research and EducationFrench-language works237,207