The search for a suitable outcome measure for use in evaluating the outcome of provision of an environmental control system
Bibliographic record
Abstract
Purpose The purpose of this paper is to report on the journey, by the Access to Communication and Technology (ACT) Service, towards a suitable measure for use in evaluating the outcome of provision of an environmental control (EC) system. Design/methodology/approach This journey has involved various approaches and methodologies. A literature search together with qualitative research, by the first author, demonstrated that the power of EC provision lies in the psycho‐social domain. Subsequently, ACT evaluated the 26‐item Psycho‐social Impact of Assistive Devices Scale (PIADS), as a research project. This was deemed to be not fit for the purpose of outcome measure in routine clinical practice. During the course of this ACT research project, a shortened version of PIADS (the PIADS‐10) was developed at the University of Western Ontario. Findings ACT has concluded that the PIADS‐10 is more likely to be fit for purpose, as it is shorter, more understandable for the patient, and easier for the clinician to administer. Originality/value Service providers and commissioners should consider PIADS‐10 as a means to evaluate outcome in EC.
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 | 0.078 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".