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Record W161509528

Age and gender differences in the use of behavior management techniques by pediatric dentists.

2007· article· en· W161509528 on OpenAlexaboutno aff
Steven M. Adair, Tara E. Schafer, Jennifer L. Waller, Roy A. Rockman

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAge groupsBehavior managementMale genderDemographyFamily medicineGerontologyDentistryDevelopmental psychology
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: This study evaluated differences in the use of behavior management techniques among older and younger male and female pediatric dentists. METHODS: We surveyed all active members of the American Academy of Pediatric Dentistry residing in the U.S. and Canada. Responses were received from 2467 (59%). The survey contained items on age, gender, and use of behavior management techniques. RESULTS: Males respondents outnumbered females 2:1. Age categories were dichotomized as < 46 and > or = 46 years. Females constituted 53% of the younger group and 14% of the older group. Four gender/age categories were used. A minority indicated that they used hand-over-mouth and active immobilization of sedated patients. No significant differences by groups were seen for use of most basic behavior management techniques. Significant differences by gender/age distribution were seen for the use of non-verbal communication and advanced techniques. Most differences in anticipated changes in technique use were age-related. Most favored parental presence in the operatory, though older males were significantly less likely to allow parental presence for some procedures. CONCLUSIONS: Some statistically significant differences in the use of behavior management techniques exist between older and younger male and female pediatric dentists. Overall, however, the 4 gender/age groups report similar frequencies of use of the techniques surveyed in this study.

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 score0.143
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.062
GPT teacher head0.262
Teacher spread0.200 · 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

Citations18
Published2007
Admission routes1
Has abstractyes

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