Health Care Professionals’ Pain Narratives in Hospitalized Children’S Medical Records. Part 2: Structure and Content
Bibliographic record
Abstract
BACKGROUND: Although clinical narratives - described as free-text notations--have been noted to be a source of patient information, no studies have examined the composition of pain narratives in hospitalized children's medical records. OBJECTIVES: To describe the structure and content of health care professionals' narratives related to hospitalized children's acute pain. METHODS: All pain narratives documented during a 24 h period were collected from the medical records of 3822 children (0 to 18 years of age) hospitalized in 32 inpatient units in eight Canadian pediatric hospitals. A qualitative descriptive exploration using a content analysis approach was performed. RESULTS: Three major structural elements with their respective categories and subcategories were identified: information sources, including clinician, patient, parent, dual and unknown; compositional archetypes, including baseline pain status, intermittent pain updates, single events, pain summation and pain management plan; and content, including pain declaration, pain assessment, pain intervention and multidimensional elements of care. CONCLUSIONS: The present qualitative analysis revealed the multidimensionality of structure and content that was used to document hospitalized children's acute pain. The findings have the potential to inform debate on whether the multidimensionality of pain narratives' composition is a desirable feature of documentation and how narratives can be refined and improved. There is potential for further investigation into how health care professionals' pain narratives could have a role in generating guidelines for best pain documentation practice beyond numerical representations of pain intensity.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".