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Record W2158059141 · doi:10.1080/13561820701795374

Exploring adolescent complementary/alternative medicine (CAM) use in Canada

2008· article· en· W2158059141 on OpenAlexafffundabout
Chris Patterson, Heather M. Arthur, Charlotte Noesgaard, Patricia Caldwell, Julie Vohra, Chera Francoeur, Marilyn Swinton

Bibliographic record

VenueJournal of Interprofessional Care · 2008
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsMcMaster University
FundersHospital for Sick Children
KeywordsContext (archaeology)Health careQualitative researchScope (computer science)LimitingMedicineAlternative medicineNaturopathyGrounded theoryMedical educationNursingPsychologyFamily medicineSociology

Abstract

fetched live from OpenAlex

A qualitative study using a grounded theory approach investigated adolescents' perceptions about complementary/alternative medicine (CAM) use. Adolescents, attending a clinic at the Canadian College of Naturopathic Medicine, were interviewed after receiving ethics approval. Data were collected using semi-structured interviews. The decision of adolescents to use CAM was based within the context of their world and how it shaped influencing factors. Factors that influenced adolescents' decision to use CAM were identified as certain personality traits, culture, media, social contacts and the ability of CAM providers to develop therapeutic relationships. The barriers and benefits of CAM use influenced evaluation of choices. The importance of barriers in limiting freedom of choice in health care decisions should be investigated by practitioners as they provide care to adolescents. Health care planning for integrative models of care requires determining the "right" blend of expertise by knowing interprofessional boundaries, determining mixed skill sets to provide the essential services and ensuring appropriate regulation that allows practitioners to use their full scope of practice.

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)0.000
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 score0.120
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.278
GPT teacher head0.366
Teacher spread0.089 · 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

Citations11
Published2008
Admission routes3
Has abstractyes

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