Association of support group leader experience with Scleroderma Patient-centered Intervention Network Support group Leader EDucation Program outcomes: Secondary analysis of a two-arm parallel partially nested randomized controlled trial
Bibliographic record
Abstract
Introduction/Objective: The Scleroderma Patient-centered Intervention Network Support group Leader EDucation Program was found in a randomized controlled trial to substantially improve leader self-efficacy. Whether the program is effective for leaders with different levels of experience, including candidate leaders with no prior experience and leaders with ⩽3 years experience or ⩾4 years experience, is not known. The objective of the present post hoc secondary analysis was to evaluate outcomes by leader experience, age, and education. Methods: The trial was a pragmatic, two-arm partially nested randomized controlled trial with 1:1 allocation to intervention or waitlist control. Eligible participants were existing or candidate support group leaders. The 13-session leader training was delivered in groups of five to six participants weekly via videoconference in 60- to 90-min sessions. The primary outcome was leader self-efficacy, measured by the Support Group Leader Self-efficacy Scale (SGLSS) immediately post-intervention. Secondary outcomes were Support Group Leader Self-efficacy Scale scores 3 months post-intervention and emotional distress, leader burnout, and volunteer satisfaction post-intervention and 3 months post-intervention. Leaders were classified as having no experience, ⩽3 years experience, or ⩾4 years experience. Results: A total of 148 participants were randomized to intervention (N = 74) or waitlist (N = 74). Compared to leaders with ⩾4 years of experience, Support Group Leader Self-efficacy Scale scores were non-statistically significantly higher post-intervention for leaders with 0-3 years experience and lower for leaders with no experience. The 3 months post-intervention Support Group Leader Self-efficacy Scale scores were significantly lower for leaders without experience and similar for leaders with 0-3 years to those with ⩾4 years experience. There were no differences by experience on other outcomes or by age and education on any outcomes. Conclusion: Support group leader education improved leader self-efficacy but was most effective for leaders with experience prior to initiating the program. Trial Registration: NCT03965780; registered on May 29, 2019.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".