Towards improving the quality of assistive technology outcomes research
Bibliographic record
Abstract
BACKGROUND: The Assistive Technology Device Outcomes Research (ATDOR) checklist was developed as a reporting guideline for researchers to enhance the quality of research in this field. The checklist contains 13 items that cover outcome domains unique to assistive technology devices (ATDs). The ATDOR was intended to be an adjunct to existing publication guidelines for outcomes research. PURPOSE: The aim of this investigation was to examine the ability of the ATDOR checklist to identify strengths and weaknesses in ATD outcomes research publications that may not be detected using another publication guideline designed for outcomes research. METHODS: Twenty original ATD outcome studies were scored using the Template for Intervention Description and Replication (TIDieR) checklist, and the ATDOR in two evaluation rounds. In the first round, articles were scored using the TIDieR alone. In the second round, they were scored using the TIDieR and ATDOR together. The difference in percentage scores between the two evaluation rounds was examined using the Wilcoxon signed rank-sum test for paired data. RESULTS: <.000). CONCLUSION: When used alongside the TIDieR, the ATDOR adds significant value to evaluations of reporting quality on assistive technology outcomes research. As this field continues to grow, researchers are invited to join in efforts to standardise reporting to promote healthier outcomes for ATD users.Implications for rehabilitationReporting guidelines that evaluate research studies enhance their reporting quality and promote healthier outcomes for ATD users.The Assistive Technology Device Outcomes Research (ATDOR) checklist was shown to be a useful tool for achieving a minimum standard of reporting in the field of assistive technology.As the field of assistive technology continues to explore different methodologies, ongoing efforts to develop and update reporting guidelines are necessary in order to capture the future needs of this research area.
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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 | 0.817 | 0.873 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.031 | 0.023 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.005 | 0.010 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".