Factors influencing retention of patient‐facing genetic counselors: Role of generational age and work environment
Bibliographic record
Abstract
Retention is a challenge that every health organization faces in an evolving and competitive market, including those in hospital settings. Some healthcare professions have identified factors that influence their employees to either stay or leave, which has led to the development and implementation of targeted strategies to increase job satisfaction and retention. Prior to this study, only factors associated with leaving have been identified in the genetic counseling profession. Despite the growing number of genetic counselors in the field, a shortage of patient-facing genetic counselors is expected by 2030. Therefore, this study explored three topics among patient-facing genetic counselors: (1) intent to stay in their current position, (2) top factors that influence this decision, (3) whether these factors differ by generational age, and (4) whether these factors differ by work setting. Genetic counselors who were in a patient-facing position for ≥6 months, board-certified, and working in the United States or Canada were eligible for study participation. Of the 520 respondents, the majority (84.6%) intend to stay in their current position. The top factors selected for staying were flexibility (58.9%), colleagues (56.4%), salary (52.0%), autonomy (48.1%), location (47.5%), and specialty (44.0%). Generation X was more likely to choose autonomy and less likely to choose location in their top five factors for staying compared to other generations. Individuals working in industry were more likely to choose flexibility and autonomy; those in academic centers were more likely to choose colleagues; those in non-hospital clinics were more likely to choose salary; and those in non-academic health centers were more likely to choose location compared to other work settings. Based on our results, clinical leadership should allocate resources to strategies that increase flexibility, foster a collaborative environment, and promote autonomy within the workplace to increase retention and prevent the predicted shortage of patient-facing genetic counselors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".