Knowledge into Action: Preliminary Results of an Assessment of Clinicians’ Intention to Use Inlow’s 60-second Diabetic Foot Screen
Bibliographic record
Abstract
In 2022, Wounds Canada updated Inlow's 60-second Diabetic Foot Screen tool to increase its functionality and, ultimately, its ease of use in clinical practice. The new version was launched at a workshop held at the Diabetes Canada national conference in Calgary, Alberta in November 2022. As a part of continuing professional development (CPD) the workshop focused on the understanding of the importance and role of diabetic foot screening in the diabetes care setting, developing skills and knowledge about when and how to screen for diabetic foot disease and understanding how to implement the diabetic foot care pathway through use of the tool. CPD encompasses the multiple educational and developmental activities that healthcare and social service professionals undertake to maintain and enhance their knowledge, skills, performance and relationships in the provision of health care and social services. Thus, as part of the processes we assessed the impact of CPD activities on participants' intentions to use Inlow's 60-second Diabetic Foot Screen after the workshop. These preliminary results illustrate the potential of using the CPD-REACTION tool, a theoretically validated questionnaire, in CPD activities for Inlow's 60-second Diabetic Foot Screen. This can improve its implementation, its scaling, and ultimately its impact on clinical practice across all care settings and on population health.
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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".