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Clinical Anatomy and the Unexpected Career: Is there a Curriculum for that?

2019· article· en· W3173349534 on OpenAlexaff
Ethan Bazos, Stefanie M. Attardi, Jennifer Baytor, Timothy D. Wilson

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsGraduation (instrument)CurriculumCareer developmentMedical educationFlexibility (engineering)PsychologyOptimismCuriosityPedagogyMedicineManagementSocial psychologyEngineering

Abstract

fetched live from OpenAlex

Traditional models of career progression: graduation, career selection, and retirement, are endangered species. Higher education institutions must adapt beyond the role of knowledge transmission and translation to prepare graduates for career evolution. Planned Happenstance Theory (PHT) is a career development model centered on the positive effects of unpredicted career‐related events occurring throughout an individual's life. Unpredictable career events, termed “Happenstance Events”, can influence career trajectory when recognized and leveraged. PHT consists of 2 competencies: (i) Actively exploring career interests and increasing “Happenstance Event” frequency, and (ii) 5 happenstance sub‐skills allowing individuals to take advantage of these events as they occur. We aimed to investigate how PHT applies to our Clinical Anatomy (M.Sc.) post‐graduate cohort to understand how these events have shaped their view of anatomy and graduate school with respect to their career goals. By doing so, we may be better able to adjust curriculum design processes and there‐by align individuals to a more fortuitous career path. Participants were Clinical Anatomy alumni (n=12) who graduated since 2014. During structured interviews, participants recalled the nature of events transpiring from beginning their graduate studies to their current position. Interviews were qualitatively examined for the presence of skills and happenstance events using phenomenological design. Following these interviews individuals were assessed for the presence of skills using the Planned Happenstance Career Inventory (PHCI). Data revealed mean PHCI skill scores: curiosity (4.4±0.3 on a 5‐point scale), flexibility (3.6±0.7), persistence (4.4±0.3), optimism (4.3±0.4), and risk‐taking (4.1±0.5). Interview coding established curiosity with highest incidence (72 ref, 31%), followed by optimism (40 ref, 20%), and persistence (40 ref, 17%). Flexibility (36 ref, 15%) and Risk‐taking (34 ref, 15%) had the lowest incidence, with a total “Planned Happenstance Skill” reference count of 233 across 8 (of 12) transcribed interviews; suggesting a “Happenstance Event” rate of 3.4 times per student. Our data suggest that Clinical Anatomy graduates exhibit high levels of Planned Happenstance Skills (curiosity, persistence, and optimism) through phenomenological study and by PHCI subscale scores. We suggest program curricula may benefit from incorporating teaching practices and opportunities integrating PHT. By providing students the metacognitive principles underlying PHT, they may be better predisposed to recognising and acting on career opportunities when they occur. If happenstance events are regarded as opportunities, and not as divergent paths from finite career trajectories, graduates will open themselves to a wider range of world views and a broader network of employers, colleagues, and/or personal business opportunities. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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