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Record W2134166282 · doi:10.7939/r3bh9j

Women in orthodontics and work-family balance: challenges and strategies

2010· article· en· W2134166282 on OpenAlexaffabout
Sarah Davidson

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBalance (ability)Work–life balanceWork (physics)ParallelsQualitative researchPsychologyMedicineFamily medicineSociologyPhysical therapySocial scienceEngineering

Abstract

fetched live from OpenAlex

The number of women entering the orthodontic profession over the past few decades has increased dramatically. A review of the literature revealed the lack of research on achieving a work-family balance among female dentists and dental specialists. Work-family balance has been researched more extensively in the field of medicine; however, despite some critical differences, parallels between these 2 professions exist. This study identified issues that Canadian female orthodontists face and strategies they use to achieve a work-family balance. A phenomenological qualitative study was used to analyze the results of semi-structured telephone interviews of a purposive sample of 13 Canadian female orthodontists. The results strongly support the role-conflict theory about the competing pressures of maternal and professional roles. Female orthodontists described their challenges and strategies to minimize role conflict in their attempt to achieve a work-family balance. The women defined balance as having success and satisfaction in both their family life and professional life. They identified specific challenges of achieving a work-family balance that are unique to orthodontic practice and strategies for adapting to their maternal and professional roles. Achieving a work-family balance is of paramount importance to female orthodontists, and the results of this study may be applied to other specialties in dentistry.

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 score0.312
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.125
GPT teacher head0.267
Teacher spread0.142 · 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

Citations20
Published2010
Admission routes2
Has abstractyes

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