The use of an online community to take the ‘Leg Club’ model to the next level
Bibliographic record
Abstract
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. The primary purpose of this research will be to evaluate the impact of an interactive online support group on diabetes knowledge, self-care behaviours, empowerment, quality of life and the healing of DFU. The secondary purpose is to determine the business potential of the online foot club by conducting a cost analysis. Such a model will deal with the geographical challenges within Canada of having a face-to-face model like the Leg Club. So can we use modern tools to aid the treatment of DFU, engaging patients and their families on an ongoing basis? Is this a potential adjunct to the Lindsey Leg Club model?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".