Exploring the readiness to practice of underrepresented healthcare workers: A scoping review
Bibliographic record
Abstract
Background: Studies across the extant literature suggest that less-experienced healthcare workers are more likely to experience adverse outcomes such as burnout, sick leaves, or intend to leave the profession. Thus, one's readiness to practice is an important element that requires more attention. While extensive research exists on the readiness of certain professions like nurses, a notable gap remains concerning other healthcare workers. Purpose: This study sought to explore studies examining readiness to practice among various underrepresented healthcare workers. Methods: We conducted a scoping review to determine the available research related to clinical and nonclinical areas related to the readiness to practice of healthcare workers, excluding physicians and nurses. We examined three databases, MEDLINE, CINAHL, and PsycINFO, from 1 January 2000 to 31 December 2023. Our search focused on readiness to practice among various healthcare professions, whether clinically focused or broadly related to professionalism. Results: Our search identified 41 articles meeting the inclusion criteria from several professions, including but not limited to physiotherapists and occupational therapists, pharmacists, osteopaths/chiropractors, and social workers. Overall, studies differed in assessing readiness to practice with a broad range of identified clinical competencies that varied between professions and regions. Nonclinical skills, such as communication, conflict management, and cultural competence, were common barriers across professions. Conclusion: Despite the heterogeneity in job roles, work settings, and geographical reasons, there is evidence to suggest that new healthcare professionals may be clinically adept but may be lacking in other nonclinical skills that could affect their work and well-being. With early-career healthcare workers particularly vulnerable to adverse outcomes in the workplace, future research should standardize core competencies, including nonclinical skills and well-being-related activities, as a prevention method across various health groups.
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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 | 0.021 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.033 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".