Put Yourself at the Helm: Charting New Territory, Correcting Course, and Weathering the Storm of Career Trajectories
Bibliographic record
Abstract
What bearing have you set you set your sights on? How do you navigate the ever-changing swells and winds of our professional landscape? Are you feeling a nebulous desire for change, that your career is not going in the direction you were expecting, worry about lack of future opportunities, or even a deep dissatisfaction in your current position? You are not alone. The formation of the Committee on Advanced Training for Certified Genetic Counselors (CATCGC) was partly in response to such sentiments, expressed within a vibrant dialogue amongst members of the genetic counseling community. The CATCGC sought to understand how genetic counselors chart courses for their careers by conducting a Decision Points exercise during a pre-conference symposium (PCS) at the 2014 NSGC Annual Education Conference. Participants were asked to identify a decision point at which they were most satisfied with their careers and one at which they were least satisfied and to describe the situation, their personal goals and intentions, any actions they took, and the outcomes. Qualitative analysis in the constructivist tradition was conducted on participants' responses and facilitators' notes from the PCS to explore what personal meanings were made of the decision points; twelve themes related to Career High Points, Low Points, and how genetic counselors made career transitions were identified. Using a constructivist framework, themes are presented in the context of the authors' personal experiences, and the authors' share their reflections on these data. We wrote this article to offer you a window into your peers' experiences - the good, the bad, and the ugly - hoping to encourage and challenge you to reflect deeply, no matter where you are on your career journey.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".