The use of an online community to take the ‘Leg Club’ model to the next level
Bibliographic record
Abstract
The use of an online community to take the 'Leg Club' model to the next level Recent robust studies (1-5) investigating the impact of peer-support patient group education models ('Leg Clubs') on chronic leg wounds demonstrate significant improvements in wound healing outcomes.Social interaction among patients is not new.It occurs routinely in the waiting areas of most clinics.The Lindsey Leg Club model took this to a more formal level, enhancing the casual nature by establishing a social vehicle allowing this interaction to happen and to include clinicians.Others have looked at how this model could be used in other clinical aetiologies, with the most obvious being diabetic foot ulcers (DFUs).Foot care education is recommended in international diabetic foot guidelines as a vital component of standard evidence-based care to prevent DFU recurrence.However, a 2014 Cochrane review concluded that while foot care education appears to have some short-term effects on patient's foot care knowledge and behaviour, there was insufficient robust evidence to conclude any impact on DFU recurrence.Can this be changed with a more socially interactive component ensuring ongoing patient engagement?A research team, at Queen's University in Canada, working in conjunction with the Canadian Association of Wound Care, has initiated a study to investigate an online peer-support 'foot club' model's impact on DFU healing.Based on the success of Leg Clubs, and studies reporting that most health consumers now use online media (6,7) to obtain their health information needs, it is hypothesised that this can make a similar impact to that of the Leg Clubs.This is most likely the first study of its kind, using online peer-support group education methods, in the management of DFU.Persons with diabetes are used to online community resources for the management and education of their diabetes.However, little, if any, information exists on the management of their DFUs.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".