A qualitative study of the language of satisfaction in children with pain
Bibliographic record
Abstract
BACKGROUND: Measures of satisfaction are essential to understanding patient experience, in general, and particularly with pain management. OBJECTIVES: (A) To identify the words children commonly use to communicate satisfaction, in general, and for pain management and (B) to determine if this vocabulary matches their caregivers. METHODS: A study of child-caregiver pairs seen at a paediatric emergency department (PED) from July to November 2014 was conducted. Children were interviewed using ten open-ended questions. Grounded theory was employed for data coding and analysis. Caregivers completed a written survey. RESULTS: A total of 105 child interviews were completed (n=53 females, mean age 9.91, SD 3.71, age range 4 to 16); 105 caregiver surveys were completed (n=80 females). Children (n=99) most commonly used 'good', 'better' and 'happy' to express satisfaction with pain management (27%, 21% and 22%, respectively), with PED care (31%, 14% and 33%) and in general (13%, 5% and 49%). Children (n=99) used the words 'sad', 'bad' and 'not good' to communicate dissatisfaction with pain management (21%, 7% and 11%, respectively) and with PED care (21%, 13% and 12%). Only 56% of children (55/99) were familiar with the word 'satisfaction'. Children's word choices were similar to their caregivers' word choices, 14% (14/99) of the time. CONCLUSION: Children use simpler words than their caregivers, including good, better and happy, when communicating satisfaction. A child's vocabulary is seldom the same as the vocabulary their caregiver uses, therefore caregiver vocabulary should not be used as a surrogate for paediatric patients. The word 'satisfaction' should be avoided, as most children lack understanding of the term.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".