A multifaceted approach to professional development to advance practice in the rehabilitative management of low trauma fracture
Bibliographic record
Abstract
Professional development activities attempt to bridge the gap between what is known through research and what is practiced in the clinical setting. Despite evidence to support the rehabilitation of low trauma fracture associated with osteoporosis, the condition is under-identified and poorly managed. This thesis investigates whether a multifaceted approach to professional development including workshops and online community of practice could advance practice in this area. The workshops were offered to rehabilitation therapists in four Ontario communities with a focus on the management of patients with low trauma fracture and osteoporosis. Workshop attendees completed pre-post and follow-up measures. A design research approach was used to examine the iterative evolution of the development of an online community of practice and the advancement of practice. Eighty-four subjects participated in the four workshops and scored significantly higher on the post tests related to knowledge and awareness of rehabilitation management of a case scenario. At six months follow-up, subjects considered their role to be important and low trauma fracture to be a priority with modest distribution of patient resources in their practice. Six rehabilitation therapists participated in the online community of practice over a seven month period. The community progressed through four cycles of innovation shifting from individual task based activity to mutual engagement in the joint enterprise of case development using evidence and theories to address authentic clinical problems. Technology played an essential role in the development and sustainability of the community of practice. The use of a design research approach allowed for just-in-time innovations for circumstances that could not be predetermined. Key words. professional development, rehabilitation, osteoporosis, knowledge translation, community of practice, technologyCreating and sustaining online communities of practice following more traditional professional development approaches holds potential for helping clinicians apply research findings to authentic clinical settings thereby contributing to the advancement of 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 | 0.041 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".