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Record W4307943436 · doi:10.3390/children9111657

Children’s Perceptions of Dental Experiences and Ways to Improve Them

2022· article· en· W4307943436 on OpenAlexafffundabout
Melika Modabber, Karen M. Campbell, C. Meghan McMurtry, Anna Taddio, Laura Dempster

Bibliographic record

VenueChildren · 2022
Typearticle
Languageen
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsHospital for Sick ChildrenUniversity of GuelphMcMaster Children's HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsPerceptionAnxietyPsychologyQualitative researchDental careNursingMedical educationDental educationMedicineFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

This qualitative study explored children's perceptions of their dental experiences and their acceptability of the CARD™ (Comfort, Ask, Relax, Distract) system, adapted for the dental setting as a means to mitigate dental fear and anxiety (DFA). A purposive sample of 12 participants (7 males) aged 8-12 years receiving dental care at the Paediatric Dental Clinic, University of Toronto, was recruited. Virtual one-on-one interviews were augmented with visual aids. Participants were oriented to and asked about their perceptions of various dental procedures. Data were deductively analyzed, according to the Person-Centered Care framework (PCC). Four themes were identified: establishing a therapeutic relationship, shared power and responsibility, getting to know the person and empowering the person. Children emphasized the importance of clinic staff attributes and communication skills. They expressed a desire to engage more actively in their own care and highlighted the positive influence of pre-operative education and preparation. Participants found the CARD™ system to facilitate opportunities for self-advocacy in their dental care.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations7
Published2022
Admission routes3
Has abstractyes

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