Factor structure and measurement invariance of post-concussion symptom ratings on the Health and Behaviour Inventory across time, raters, and groups: An A-CAP study
Bibliographic record
Abstract
OBJECTIVES: To validate the two-factor structure (i.e., cognitive and somatic) of the Health and Behaviour Inventory (HBI), a widely used post-concussive symptom (PCS) rating scale, through factor analyses using bifactor and correlated factor models and by examining measurement invariance (MI). METHODS: PCS ratings were obtained from children aged 8-16.99 years, who presented to the emergency department with concussion (n = 565) or orthopedic injury (OI) (n = 289), and their parents, at 10-days, 3-months, and 6-months post-injury. Item-level HBI ratings were analyzed separately for parents and children using exploratory and confirmatory factor analyses (CFAs). Bifactor and correlated models were compared using various fit indices and tested for MI across time post-injury, raters (parent vs. child), and groups (concussion vs. OI). RESULTS: CFAs showed good fit for both a three-factor bifactor model, consisting of a general factor with two subfactors (i.e., cognitive and somatic), and a correlated two-factor model with cognitive and somatic factors, at all time points for both raters. Some results suggested the possibility of a third factor involving fatigue. All models demonstrated strict invariance across raters and time. Group comparisons showed at least strong or strict invariance. CONCLUSIONS: The findings support the two symptom dimensions measured by the HBI. The three-factor bifactor model showed the best fit, suggesting that ratings on the HBI also can be captured by a general factor. Both correlated and bifactor models showed substantial MI. The results provide further validation of the HBI, supporting its use in childhood concussion research and clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".