Experiences of using an Environmental Control System (ECS) for persons with high cervical spinal cord injury: the interplay between hassle and engagement
Bibliographic record
Abstract
PURPOSE: Environmental Control Systems (ECS) have many benefits; however studies of personal experience of ECS use are scarce. This qualitative study explored the insiders' experience of using an ECS. METHOD: An ECS starter-pack was compiled and trialled for an eight-week period with six persons with high spinal cord injuries (SCI) living in Ireland. Semi-structured interviews were subsequently completed with each person and analysed using Interpretative Phenomenological Analysis (IPA). FINDINGS: Two major themes emerged: "Taking back a little of what has been lost", and "Getting used to ECS" which is the focus of the current paper. This theme captured a dynamic interplay between the experience of "hassle" and "engagement" for new users of ECS. "Hassle" resulted from technological frustrations and the challenge of breaking familiar habits, while "Engagement" resulted from feeling good, having fun and being surprised. CONCLUSIONS: The complex interweaving of hassle and engagement experienced by new ECS users reflects the clinical experience of rehabilitation providers. The importance of overcoming initial hassle needs to be understood by clinicians and users in order to maximise the potential benefit of ECS. Non-use must be considered one reasonable outcome if based on realistic ECS trials.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".