Peer mentoring in rehabiltation of spinal cord injured persons
Bibliographic record
Abstract
Introduction: The incidence of spinal cord injury (SCI) in DK is 130/yr, prevalence is 3000. Uncertainties about prognosis and future options are key terms for patients in neurorehabilitation, and more knowledge is needed in this area. In a joint project between The Danish Spinal Cord Injuries Association (RYK) and the two nationwide SCI neurorehabilitation centers, we tested and evaluated the role of peer mentoring as supplement to professional rehabilitation efforts. Methods: In an interventional study, newly-diagnosed SCI patients were offered one - three meetings with a peer mentor during an inclusion period of 1 year, expecting 50 participants (mentees). We planned to examine the individual gains from mentoring and participants´ satisfaction regarding the organization of mentoring in a neurorehabilitation hospital setting. Outcome measures were QoL, pain scores and information regarding issues, addressed during mentor sessions. Non-participants were asked to complete a questionnaire in order to describe the group (gender, age, etiology). Results: We established and educated a corps of volunteer mentors (n=57, 37 men and 20 women, aged 20 – 76 years). Preliminary results: 53 mentees have participated: 33 (62%) men and 20 (38%) women aged 19.5 – 77.0 years, mean age 47.4 years at time of intervention. 43% traumatic and 57% non-traumatic SCI. Conclusions: The project closed ultimo December 2016. Results will be presented in scientific and layman´s journals. The mentors and their efforts have been well received, and we find there is a need for further studies exploring the role and extent of peer support after discharge from neurorehabilitation centers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".