Genetic counselors with advanced skills: II. A new career trajectory framework
Bibliographic record
Abstract
Career development frameworks help professionals better understand career decision-making, but the genetic counseling field lacks a comprehensive framework to describe career development. The purpose of this qualitative study was to explore commonalities across participants' career trajectories and identify factors which influence decision-making throughout genetic counseling careers. Using purposive sampling, 17 genetic counselors with advanced skills were interviewed about their career trajectories, factors which influenced career decisions, and the process and outcomes of those decisions. Content analysis was both inductive and deductive, employing triangulation techniques to enhance analytic rigor. Results highlighted common experiences and critical processes of interviewees' career trajectories and contributed to the development of the Genetic Counselor Career Trajectory Framework (GCCTF), which depicts an iterative process of considering change in one's career. Each iteration is prompted by predisposing influences (past experiences, personal attributes, and contextual factors), characterized by self-assessment and flexible planning, and completed when a decision about making a change is reached. Multiple iterations collectively create evolution of a career trajectory. The GCCTF adds to existing theories of career development by emphasizing dynamic processes of considering change and applies established concepts to a specialized healthcare profession. Individual genetic counselors can utilize the GCCTF to expand awareness of factors influencing a specific career decision and gain insight into experiences of change across their careers.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".