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Record W4317794536 · doi:10.3138/jvme-2022-0095

Development and Evaluation of an Experiential Career Planning Assignment to Train Students to Assess Organizational Fit

2023· article· en· W4317794536 on OpenAlexvenueno aff
Amy M. Snyder, Jennifer R. Hartwell

Bibliographic record

VenueJournal of Veterinary Medical Education · 2023
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceCareer planningMedical educationExperiential learningCareer developmentWorkforce planningPsychologyLifelong learningPedagogyMedicinePolitical science

Abstract

fetched live from OpenAlex

Determining if an employment opportunity will be a good match can feel daunting, especially for veterinary graduates entering the workforce. To ease this transition, veterinary educators traditionally have attempted to provide career support through interspersed didactic lectures on career options and the preparation of employment documents. While well intended, this approach fails to address the multiple dimensions of effective career planning or the reality that career planning is a lifelong endeavor. For a career-planning teaching modality to be effective, it must address all stages of career planning and provide a framework that can be adapted throughout a career. Here we describe how a four-stage career-planning model, utilized throughout higher education, was employed to create a career planning assignment for guiding students in assessing organizational fit. We describe how student feedback was used to inform revisions, resulting in an improved educational experience as measured by students' perceptions of the utility of the assignment. Additional recommendations based on instructor reflection are provided to guide creation and implementation of future assignments. Given the growing support for professional skills training in veterinary medical education, we view incorporation of such learning activities as essential to preparing students to enter the modern veterinary workplace.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
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 head0.706
GPT teacher head0.637
Teacher spread0.069 · 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 designQualitative
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

Citations2
Published2023
Admission routes1
Has abstractyes

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