Construct validity and impact of mode of administration of the PedsQL™ among a pediatric injury population
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine the construct validity of the PedsQL™ health related quality of life (HRQoL) instrument for use among injured children and to examine the impact of using different modes of administration, including paper and pencil, online and telephone. METHODS: Two hundred thirty-three participants (aged 0 - 16) were recruited from hospital wards and the emergency department of a pediatric hospital in a large urban center in British Columbia, Canada. Data used to evaluate the construct validity of the PedsQL™ were collected from participants at the time of seeking injury treatment (baseline) to capture a retrospective measure of pre injury health, and one month post injury. Data used to compare different modes of administration (n = 44) were collected at baseline. To assess construct validity repeated measures analysis of variance (rANOVA) was used to determine whether the PedsQL™ tool was able to discriminate between patients pre and post injury while investigating possible interaction by category of length of stay in hospital. The impact of different modalities of administering the PedsQL™ on item responses was investigated using Bland-Altman plots. RESULTS: rANOVA showed significant differences in PedsQL™ total score between baseline and one month post injury (p < .001), and differences in mean total score at one month post injury by category of injury severity (p < .001). There was also significant interaction by category of injury severity for the change in PedsQL™ total score from baseline to one month (p < .001). Pearson's correlations were highly significant across three modalities of survey administration: paper and pencil, computer and telephone administration (range: .92 to .97, p < .001). Bland-Altman plots showed strong consistency. CONCLUSION: The PedsQL™ instrument is able to discriminate between pre and post injury HRQoL, as well as HRQoL post injury for injuries of varying severity. These findings are an indication that this instrument has good construct validity for the purpose of evaluating HRQoL of injured children. Data collected via paper-pencil, online and telephone administration were highly consistent. This is important as depending on the setting, clinical or research, different modalities of completing this instrument may be more appropriate.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".