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Record W2212805733

Complementary and alternative medicine: A survey of its use in children with chronic respiratory illness.

2014· article· en· W2212805733 on OpenAlexaffabout
Ellison Richmond, Denise Adams, Simon Dagenais, Tammy Clifford, Lola Baydala, W. James King, Sunita Vohra

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsUniversity of OttawaUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsMedicineSubspecialtyFamily medicineMedical prescriptionAsthmaAlternative medicineHealth careNursingInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The use of complementary and alternative medicine (CAM) has increased in recent years, with especially high prevalence in individuals with chronic illnesses. In the United States, the prevalence of CAM use in pediatric asthma patients is as high as 89%. OBJECTIVE: To investigate the epidemiology of pediatric CAM use in respiratory subspecialty clinics. METHODS: A survey was conducted at two hospital-based respiratory clinics in Edmonton (Alberta) and Ottawa (Ontario). Caregivers (most often parents) of children <18 years of age were asked questions regarding child and caregiver use of CAM, including products and practices used, beliefs about CAM, trust in information sources about CAM and characteristics of the respondents themselves. RESULTS: A total of 202 survey questionnaires were completed (151 from Edmonton and 51 from Ottawa). Pediatric CAM use in Edmonton was 68% compared with 45% in Ottawa, and was associated with caregiver CAM use, poorer health and health insurance coverage for CAM. The majority (67%) of children using CAM had taken prescription drugs concurrently and 58% of caregivers had discussed this with their doctor. DISCUSSION: Lifetime use of CAM at these pediatric clinics was higher than reported for children who do not have chronic diseases. CAM practices that are popular may be worthy of further research to evaluate their effectiveness and safety profile with regard to drug interactions. Health care providers should be encouraged to discuss CAM use at every visit, and explore their patient's health-related beliefs, behaviours and treatment preferences.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.300
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2014
Admission routes2
Has abstractyes

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