Use of Complementary and Alternative Medicine in a General Pediatric Clinic
Bibliographic record
Abstract
BACKGROUND: Use of complementary and alternative medical therapies is common and increasing, particularly for children with chronic disease. OBJECTIVES: The purpose of this work was to describe the use of complementary and alternative medicine by children and to identify factors that may influence the use of complementary and alternative medicine. PATIENTS AND METHODS: We conducted a cross-sectional descriptive study with children who were visiting a pediatric outpatient clinic. Parent's satisfaction about primary care was evaluated with the Parent's Perceptions of Pediatric Primary Care Quality questionnaire. RESULTS: Fifty-four percent of children used > or = 1 type of complementary and alternative medicine in the previous year. No sociodemographic characteristic difference was found between user and nonuser groups. Children most often used complementary and alternative medicine to treat musculoskeletal problems (27%), psychological problems (24%), or infections (20%). Factors that influenced complementary and alternative medicine use were "word of mouth" (36%), "reference by a physician" (28%), "personal experience by parents" (28%), and "no adequate resources in 'traditional' medicine" (21%). Forty-seven percent of complementary and alternative medicine users used prescribed medications simultaneously. Most users (75%) believed that complementary and alternative medicine had no potential adverse effects or interactions with prescribed medication. Only 44% of complementary and alternative users were known as such by their physician. The primary care satisfaction was significantly lower in complementary and alternative users versus nonusers. Parents of complementary and alternative users were less satisfied in the areas of accessibility, knowledge of the patient, and communication. CONCLUSIONS: Complementary and alternative medicine was used by 54% of the children in our cohort. Complementary and alternative medicine users were less satisfied with primary care than nonusers. Only 44% of complementary and alternative medicine users were known by their physician. It is important that physicians systematically elicit families' expectations of treatment and be aware of the range of therapies used by children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".