Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".