Validation of Hospitalization Impact Scale among families with children hospitalized for cancer treatment
Bibliographic record
Abstract
AIM: To make further modifications to and validate the psychometric properties of the Hospitalization Impact Scale. BACKGROUND: A child's repeated and prolonged hospitalization for cancer treatment can result in great changes for the entire family. A hospitalization-specific screening tool is needed to help clinical nurses identify families who are experiencing major impacts during their child's hospitalization. DESIGN: A cross-sectional study was employed to examine the psychometric properties of the Hospitalization Impact Scale. METHOD: The sample consisted of 253 families with children hospitalized for cancer treatment in four paediatric oncology departments in four hospitals in mainland China from September 2013 - March 2014. Parents completed the 36-item Hospitalization Impact Scale, demographic measures and the Family Impact Module of the Pediatric Quality of Life Inventory. Reliability, construct validity, known-group validity and concurrent validity were examined. RESULT: The revised Hospitalization Impact Scale included six factors containing 34 items. It demonstrated sound concurrent validity with the Family Impact Module of the Pediatric Quality of Life Inventory and excellent internal consistency. CONCLUSION: The revised Hospitalization Impact Scale met the standard psychometric criteria for reliability and validity. Thus, it could be applied in paediatric oncology departments to help nurses assess and identify families experiencing major impacts during a child's hospitalization for cancer treatment.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".