Clinical Anatomy and Unexpected Careers: Is There Curriculum for That?
Bibliographic record
Abstract
Reduction in faculty positions in higher education and increased graduate matriculation rates represent a higher education conundrum. Planned happenstance theory (PHT) is a career development model focusing on positive outcomes resulting from unpredictable precareer events. This mixed methods study explores how PHT applies to the career paths of a clinical anatomy (CA) postgraduate cohort. It provides insight into educational practices designed to equip students for labor markets inside and outside academia. Alumni of CA (n = 12; 2014-2018) were interviewed about career-related events transpiring from graduate studies to present, allowing exploration on how PHT contextualizes their shared experiences. Planned happenstance career inventory (PHCI) enumerated planned happenstance skill (PHS) scores. Total PHS was referenced 527 times across 12 interviews. Of the PHS references, curiosity established highest incidence (154 references, 29%), optimism (132 references, 25%), flexibility (101 references, 19%), risk-taking (85 references, 16%), and persistence (55 references, 10%) and 43 distinct happenstance events were documented. In addition, social networking (52 references) arose as an emergent code and was divided into internal networking (28 references, 54%) and external networking (24 references, 46%). Application of the five-point PHCI scale revealed: curiosity (4.4 ± 0.3; mean ± SD), flexibility (3.6 ± 0.7), persistence (4.4 ± 0.3), optimism (4.3 ± 0.4), and risk-taking (4.1 ± 0.5). Curiosity had the strongest association with happenstance event incidence. Social networking was a key substituent of PHT not yet described in the literature. Educational practices incorporating PHT concepts, with emphasis on curiosity, may provide graduates novel metacognitive skills needed to develop novel career paths.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".