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Record W2936557960 · doi:10.33805/2572-6978.103

Managing Stress, Distress and Coping Strategies in Dentistry

2017· article· en· W2936557960 on OpenAlexaff
Louis Touyz ZG

Bibliographic record

VenueDental Research and Management · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsMcGill University
Fundersnot available
KeywordsDistressStressorCoping (psychology)Dysfunctional familyPsychologyMoodPreparednessStress managementEmotional distressSocial psychologyPsychiatryClinical psychologyAnxietyPolitical science

Abstract

fetched live from OpenAlex

Background: Dysfunctional social behavior deriving from work distress is common among practicing dentists. 1.2 Aim: This paper appraises prevalent stressors for practicing dentists, not only in North America, but also in dental practices in all other continents. This critique aims to describe from a dentists’ viewpoint, what is wrong, why it is wrong and what can be done about it. Deconstruction of stressors: Among the main reasons are misdirected motivations, unfulfilled performances, inadequate coping strategies, unsatisfied needs and frustrations arising from unreasonable expectations. Social changes, financial constructs and professional stressors can all play a part. Discussion: Abuse by financiers, patients and staff, with inadequate skills, muddled management of resources and jumbled attitudes, may precipitate anything from unexplained mood changes to psychotic episodes. These forces may work to convert stress to distress. Concluding remarks: Hopefully this exposition provides answers, novel thinking, fresh insights, orderly approaches, practical skills and coping strategies for dentists to improve their role as health care providers in a community.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.999

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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
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 head0.162
GPT teacher head0.538
Teacher spread0.376 · 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.

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

Citations3
Published2017
Admission routes1
Has abstractyes

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