Revisiting the meaning and the source of health‐related constructs and their applications in neurodisability
Bibliographic record
Abstract
The aim of this review is to revisit the meaning of common concepts and frameworks promoted to capture subjective outcomes of patients, the content of their corresponding measurements, and the preferred sources of the information of interest. This is important because conceptualizations of 'health' and the subject evaluations thereof continue to evolve. Related but distinct concepts like quality of life (QoL), health-related QoL (HRQoL), functional status, health status, and well-being are often used indiscriminately to assess clinical impacts of interventions and to influence decisions about patient care and policymaking. The discussion addresses and illustrates the following issues: (1) the required features of effective and valid health-related concepts; (2) understanding underlying factors that often create confusion about QoL and HRQoL; and (3) how these concepts provide insight into, and promote, health in the context of populations with neurodisability. The hope is to illustrate how a combination of a clear research question, a hypothesis, conceptualization of the required outcomes, and operational definitions of the domains and items of interest, including item mapping, can help to achieve robust methodology and valid findings beyond the required psychometric properties. WHAT THIS PAPER ADDS: The language, content, and the source of perceived health and life issues are clarified. Using the same terms for different constructs, or different terms for the same constructs, creates confusion and hinders outcome research. The challenges of using patient-reported outcomes in neurodisability are addressed.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".