The Effectiveness of Blended Online Do-Live-Well Workshops for Occupational Therapists
Bibliographic record
Abstract
The Do-Live-Well (DLW) framework was developed by occupational therapists (OTs) to demonstrate how engaging in various activities can impact health and well-being of people. The DLW team has provided education for OTs across the world, and there has been a need for online learning with synchronous features. This study aimed to examine the impact of blended online DLW workshops that combined asynchronous and synchronous educational formats for OTs. We evaluated changes in participants’ knowledge of the DLW framework, factors influencing framework adoption in practice, satisfaction with the workshop, and actual use of the framework. Descriptive statistics were used for the continuous variables, and frequencies were used for discrete variables. We then applied one-way repeated ANOVA to examine the changes in knowledge over time. Participants’ knowledge levels increased immediately after the workshop (p < .001) but decreased after three months (p = .003). Their perceived ability to apply the DLW framework in practice increased after the workshop (pre-test, mean = 32.32/60, SD = 6.25; post-test, mean = 44.11/60, SD = 4.76; follow-up, mean = 39/54, SD = 6.97). Participants reported a high level of satisfaction with the workshop (mean = 107/112, SD = 6.24). Blended online workshops can be as effective as asynchronous online and in-person workshops, evidenced by increased knowledge, improved perceived ability to apply the framework in practice, and high scores for participants’ satisfaction with the workshops. In future workshops, it will be important to consider how to facilitate knowledge retention and framework application in clinical practice.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".