Evaluating Impact of an Online Leadership Course for Newly Graduated Occupational Therapists
Bibliographic record
Abstract
Leadership has been established as a key competency for occupational therapists, however the literature on how occupational therapists conceptualize leadership is limited. Additionally, there is a dearth of literature on best practices to develop occupational therapists’ leadership skills and there is yet to be an evaluation of an online educational approach. This study aimed to understand if an online educational platform contributed to the development of leadership skills among occupational therapists. Participants were newly graduated occupational therapists who participated in a four-week Leadership Skills Development massive open online course (MOOC). This study used a mixed methods quasi-experimental design with two intervention cohorts and time series delay. All participants completed the MindTools Leadership Skills Assessment, the Authentic Leadership Self-Assessment Questionnaire and open-ended self-reflection questions. Analysis of the data was conducted using Rstudio, for the quantitative data, and thematic coding, for the qualitative data. The results showed there was not a significant change in scores on the leadership evaluation tools over time. However, cohort A demonstrated an increase in self-awareness and cohort B showed increases in balanced processing, providing support, and stimulation. Both cohorts showed increases in relational transparency. Participants reported that participating in the MOOC provided education on how to further develop leadership skills. Leadership skill development takes time to develop and further evaluation is required to determine if these skills continue to develop when put into practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".