The development of the spinal cord injury participation and quality of life (PAR-QoL) tool-kit
Bibliographic record
Abstract
Purpose: Accurate descriptions of the impact of spinal cord injury (SCI)-related secondary health conditions (SHCs) on quality of life (QoL) are important to help guide the direction of resources and evaluation of therapies. However, selecting an appropriate outcome tool can be a challenge due to several clinical, theoretical and measurement issues. In order to help improve practices related to QoL measurement, a web-based Participation and QoL (PAR-QoL) tool-kit was designed to support researchers and clinicians with the outcome measure selection process. Method: The content of the PAR-QoL website (www.parqol.com) was developed through a series of systematic reviews of the SHC literature. Outcome tools identified in the studies were classified using Dijker’s (2005) theoretical framework. Results: A total of 199 studies were identified and categorized across eight different SHCs. Measures from the studies were extrapolated, and details regarding their [1] sensitivity to SHC impact [2], psychometric properties for SCI and [3] underlying QoL constructs were summarized onto a website. Conclusions: A better understanding of SHC impact on QoL will improve the quality of research, which in turn may provide better evidence for securing the necessary resources to help persons with SCI manage their health.Implications for RehabilitationAssessing the impact of secondary health conditions on quality of life (QoL) in persons with spinal cord injury (SCI) is a challenge.A number of theoretical, measurement and clinical issues should be taken into account when selecting a QoL outcome tool.Developing innovative approaches to knowledge transfer should be guided by an implementation strategy to maximize successful uptake by targeted stakeholders.The participation and quality of life (PAR-QoL) website (www.parqol.com) is an on-line educational resource for clinicians and researchers working in the SCI field to promote better practices in the QoL outcome tool selection process.
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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 | 0.029 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".