Development of Emotional Well-Being indicators to advance the quality of spinal cord injury rehabilitation: SCI-High Project
Bibliographic record
Abstract
Context: Emotional Well-Being (EWB) post-spinal cord injury or disease (SCI/D) is a critical aspect of adjustment to disability. Advancing care and assuring equity in care delivery within this rehabilitation care domain is essential. Herein, we describe the selection of EWB structure, process and outcome indicators for adults with SCI/D in the first 18 months after rehabilitation admission.Methods: A pan-Canadian Working Group completed the following tasks: (1) defined the EWB construct; (2) conducted a systematic review of available outcomes and their psychometric properties; (3) constructed a Driver diagram summarizing available evidence associated with EWB; and, (4) prepared a process map. Facilitated meetings allowed selection and review of feedback following rapid-cycle evaluations of proposed structure, process and outcome indicators.Results: The structure indicator is the proportion of staff with appropriate education and training in EWB and access to experts and resources. The process indicator is the proportion of SCI/D patients who were screened for depression and anxiety symptoms at rehabilitation admission and rehabilitation discharge. The intermediary outcome is the proportion of SCI/D patients at risk for depression or anxiety at rehabilitation discharge based on screening symptom scores. The final outcomes are: (a) proportion of individuals at risk for depression or anxiety based on screening symptom scores; and (b) proportion of individuals who received referral for EWB services or intervention.Conclusion: The proposed indicators have a low administrative burden and will ensure feasibility of screening for depression and anxiety at important transition points for individuals with SCI/D. We anticipate that the current structures have inadequate resources for at-risk individuals identified during the screening process.
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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 | 0.022 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| 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".