Personal and professional factors influencing career choice regret during the COVID-19 pandemic
Bibliographic record
Abstract
Objective: Many healthcare workers (HCWs) have been subjected to additional workplace and personal stressors during the COVID-19 pandemic. Some stressors may be more likely to contribute to career choice regret and the decision to leave the healthcare profession. Loss of critical numbers of personnel could leave healthcare systems without a ready, capable workforce. The purpose of this research was to determine which personal and professional characteristics increased frequency of career choice regret.Methods: An international, cross-sectional survey was conducted to determine if specific personal and professional characteristics were associated with career choice regret and intent to leave. One short-answer item was included in the 20-item survey. The sample consisted of 874 English-speaking HCWs from 18 countries with representation of various practice settings, disciplines, ages, and years in healthcare.Results: Significant correlations between pre- and intra-pandemic frequency of thoughts about leaving the healthcare profession were observed. Ordinal regression analyses were conducted, finding significant relationships between career regret thoughts and female gender, age, and death of a family member or friend. Death of a coworker was not associated with increased thoughts of leaving the healthcare profession. Themes from the short-answer item included fatigue, anger, doubt, fulfillment, and the pandemic as a journey.Conclusions: Healthcare systems may be vulnerable to a loss of HCWs due to the effects of working during the COVID-19 pandemic. Hospital and health system executives need to understand the current threats to the stability of the workforce and develop strategies to prevent attrition of skilled, capable professionals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".