Health Care Professionals’ Pain Narratives in Hospitalized Children’s Medical Records. Part 1: Pain Descriptors
Bibliographic record
Abstract
BACKGROUND: Although documentation of children's pain by health care professionals is frequently undertaken, few studies have explored the nature of the language used to describe pain in the medical records of hospitalized children. OBJECTIVES: To describe health care professionals' use of written language related to the quality and quantity of pain experienced by hospitalized children. METHODS: Free-text pain narratives documented during a 24 h period were collected from the medical records of 3822 children (0 to 18 years of age) hospitalized on 32 inpatient units in eight Canadian pediatric hospitals. A qualitative descriptive exploration using a content analysis approach was used. RESULTS: Pain narratives were documented a total of 5390 times in 1518 of the 3822 children's medical records (40%). Overall, word choices represented objective and subjective descriptors. Two major categories were identified, with their respective subcategories of word indicators and associated cues: indicators of pain, including behavioural (e.g., vocal, motor, facial and activities cues), affective and physiological cues, and children's descriptors; and word qualifiers, including intensity, comparator and temporal qualifiers. CONCLUSIONS: The richness and complexity of vocabulary used by clinicians to document children's pain lend support to the concept that the word 'pain' is a label that represents a myriad of different experiences. There is potential to refine pediatric pain assessment measures to be inclusive of other cues used to identify children's pain. The results enhance the discussion concerning the development of standardized nomenclature. Further research is warranted to determine whether there is congruence in interpretation across time, place and individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".