Impact of Continuous Professional Education on Leadership for Rehabilitation Professionals in Bangladesh: A Pilot Study
Bibliographic record
Abstract
Background: In healthcare and rehabilitation profession, continuous professional education (CPE) is deemed as an essential tool by institutions, regulatory bodies and organizations. It is considered to be a safeguard to professional practice to ensure patient safety and efficient service delivery. Leadership program is an emerging subject that integrates technical, management and leadership skill that found indispensable to enhance ultimate professional development. Objective: The purpose of this article is to explore the potential impact of CPE on leadership training for rehabilitation professionals in Bangladesh. Methodology: This pilot study was a mixed methods study. 20 rehabilitation professionals participated in the study. Quantitative data were collected by structured questionnaire and qualitative data were obtained by face to face video recording interviews. Quantitative data were analyzed by Statistical Package for the Social Sciences (SPSS) and thematic analysis was considered for qualitative data. Result: Study findings showed that 95% of the participants (n=19) were in their 2nd to 3rd decade of life, there mean age were 26 and male participation (n=12) were higher than female (n=8). Highest academic qualification was master’s degree and none of the participants attended leadership training before. Overall objective, planning and atmosphere of the training course, quality of trainers and time management seemed effective and satisfactory to the participants they agreed with. Leadership qualities and elements have profound impacts on knowledge and attitude towards positivity in performing professional duties and Responsibilities. The training also enhances motivation to be a skilled professional in clinical practice, also encourages serving with outmost excellence towards patients or clients. Conclusion: The study demonstrates that education on leadership to professionals have an impact on motivation and positivity towards developing technical, management and professional skills. The study may have impact on developing diverse qualities of a professional in personal and organizational perspective; this can be explored by continuing education on leadership and evaluation of the behavioral changes, professional and management skills in rehabilitation professionals’ serving in an organization in longer run.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".