Caring for Persons with Spinal Cord Injury: A Mixed Study Evaluation of eLearning Modules Designed for Family Physicians
Bibliographic record
Abstract
Abstract Background : Family physicians often do not feel comfortable or have the knowledge or experience to adequately treat and manage the needs of persons with Spinal Cord Injury. An eLearning resource was designed to provide family physicians with accessible information to facilitate their treatment of persons with Spinal Cord Injury. Methods : This study evaluated the effectiveness of eLearning modules with regard to meeting the learning needs of family medicine residents treating individuals with spinal cord injury. A mixed methods approach, involved collecting and analyzing data from post module quantitative surveys and qualitative interviews. The constructs of the W(e)Learn framework guided data analysis. Findings : Family medicine residents reported they enjoyed the learning experience, learned new information and raised their awareness of specific health care needs with regard to treating and managing persons with spinal cord injury. Residents confirmed designing the resource to be accessed anytime and anywhere will enable them to retrieve information on a need to know basis. A few residents provided examples of how they applied information they learned as a result of completing the resource. Conclusion : Effectively designed eLearning modules that address learner needs can be a viable approach to providing information to physicians regarding treating and managing persons with spinal cord injury.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".