Usability of the Participation and Quality of Life (PAR-QoL) Outcomes Toolkit Website for Spinal Cord Injury
Bibliographic record
Abstract
Background: Quality of life (QoL) is an important parameter to monitor during rehabilitation; however, accurate assessment is challenging. Among individuals with spinal cord injury (SCI), assessing QoL is further challenged due to complex sequelae, such as secondary health conditions and factors related to community integration. A Participation and Quality of Life (PAR-QoL) toolkit was created to aid clinicians and researchers in the selection of QoL outcomes tools specific to SCI. Objectives: The aim of this study was to evaluate the use and usability of the PAR-QoL toolkit. Methods: A cross-sectional study was conducted using an online survey from December 2013 to November 2016. Google Analytics were collected from April 2012 to April 2018. Survey sections addressed “use” (behavioral practices and actual use) and “usability” (perceived ease of use and perceived usefulness). Any person who visited the PAR-QoL website was invited to complete the survey. Summary statistics and percent concordances were calculated to describe results from the survey and Google Analytics. Results: The PAR-QoL website had 188,577 users. The five most visited webpages were outcome tools, with bounce rates ranging from 77% to 90%. Of the 46 survey respondents, 67% were not current users of the PAR-QoL website, and 87% intended to use the resources in the future. Conclusion: Uptake of the PAR-QoL website is currently limited. Usability of the PAR-QoL website may be improved by modifying navigation, removing the “less useful” components, ensuring regular updates of content and resources, and promoting the website.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".