Consensus building for the development of guidelines for recommending mobility service dogs for people with motor impairments
Bibliographic record
Abstract
BACKGROUND: According to recent research, mobility service dogs (MSD) improve grasping ability, autonomy in ADL, manual wheelchair propulsion, walking, transfers, psychosocial aspects, reintegration into normal life, and satisfaction with important occupations, and decrease pain in manual wheelchair users' shoulders/wrists.However, it remains a challenge for rehabilitation professionals to recommend MSD for different profiles of neurological disorders.OBJECTIVE: Formulate guidelines to support the decision-making process of rehabilitation professionals recommending MSD.METHODS: Focus groups with MSD experts (7 therapists, 4 trainers, 3 managers, 5 users) responded to four research questions.They had to formulate and prioritize criteria to inform the recommendation of MSD for three clinical cases: A-tetraplegia with powered wheelchair, B-paraplegia with manual wheelchair, and C-ambulatory (incomplete SCI or neurodegenerative disease).RESULTS: For the decision-making process of recommending MSD, six main variables were identified: scientific evidence cited (they are different among clinical cases), added value of MSD compared to other assistive devices (dissimilar among clinical cases), prioritization of personal (7), environmental (8) and canine (6) characteristics, and possible negative consequences in MSD user's life (stigmatization, resilience, care burden, authority or obedience). CONCLUSIONS:The results provide the basis for the development of clinical practice guidelines for occupational therapists and physiotherapists recommending MSD to individuals presenting various profiles of neurological disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.243 | 0.346 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.014 | 0.014 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".