Bibliographic record
Abstract
OBJECTIVE: To determine parents' attitudes toward and acceptance of waiting times for their child's operation. DESIGN: Waiting times were measured by a cross-sectional method. A descriptive survey was conducted of families with a child waiting for a non-urgent operation. SETTING: A university teaching hospital. SUBJECTS: Parents of children (age < 20 yr) waiting for non-urgent pediatric general-surgery operations. MAIN OUTCOME MEASURES: Parents' concerns and attitudes about waiting for their child's operation, how it was affecting the child and family, how urgent they felt the need for surgery was, and what they thought was a reasonable maximum waiting period. RESULTS: Of 89 patients waiting for non-urgent pediatric general-surgery operations at the time of the survey, 61% had been waiting > 6 months and 30% > 12 months. Of the 57 families (64%) who returned completed surveys, 94% reported the wait to be emotionally stressful for the family; 81.5% expected their child's quality of life would improve after the operation. As for length of wait, 83% felt that > 3 months was unacceptable, and 98% > 6 months. CONCLUSIONS: Parents of children waiting for pediatric general surgery operations thought that the need for the operation was significantly more urgent then their classification of elective. They felt that waiting periods should not exceed 3 months. Long waiting periods are stressful for both family and child. Parental perceptions are important when considering strategies for wait-list management.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".