The spinal cord injury (SCI) peer support evaluation tool: the development of a tool to assess outcomes of peer support programs within SCI community-based organizations
Bibliographic record
Abstract
STUDY DESIGN: Guided by the 4-step process outlined in the Consensus-based Standards for the selection of health Measurement INstruments (COSMIN) guideline, multiple methodologies were used: Delphi, literature reviews, ratings with consensus, think-aloud, and test-retest. OBJECTIVES: The purpose of this study was to develop and test a spinal cord injury (SCI) peer support evaluation tool that meets the needs of community-based SCI organizations in Canada. SETTING: Peer support programs for people with SCI delivered by community-based SCI organizations. METHODS: This research was co-constructed with executives and staff from SCI community-based organizations, people with SCI, researchers, and students. Given the multiple steps of this study, sample size and characteristics varied based on each step. Participants included people with SCI who received peer support (mentees) or provided peer support (mentors/supporters) and staff of community-based organizations. RESULTS: In step 1, the 20 most important outcomes for SCI peer support were identified. In step 2 and 3, the 97 items were identified to assess the outcomes and by using rating and multiple consensus methodologies 20 items, one to assess each outcome, were selected. In step 4, content and face validity and test-retest reliability were achieved. The resulting SCI Peer Support Evaluation Tool consists of 20 single-item questions to assess 20 outcomes of SCI peer support. CONCLUSION: Through a systematic process, the SCI Peer Support Evaluation Tool is now ready to be implemented to assess outcomes of SCI peer support programs delivered by community-based SCI organizations.
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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 | 0.113 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".