Occupational stress among Canadian orthodontists.
Bibliographic record
Abstract
The occupational stress associated with many professions, including general dentistry, has been well researched. An anonymous, self-administered, mail-out survey was distributed to Canadian orthodontists. The survey included 67 potential stressors, an overall occupational stress score, an overall job satisfaction scale, and items addressing various characteristics of the respondents. The response rate was 51.2% (335/654). Pronounced differences were found between the respondents in the evaluation of potential stressors and the overall occupational stress score. The category of stressors with the highest mean severity of stress scores was time-related stressors. The stressors with high mean severity scores and high mean frequency scores were as follows: falling behind schedule, trying to keep to a schedule, constant time pressures, patients with broken appliances, and motivating patients with poor OH and/or decalcification. Stepwise multiple regression determined a model, involving overall job satisfaction, age, participation in a study group, hours worked per week, part-time academics, days of continuing education per year, and participation in stress management, to account for 35.9% of the variation in overall occupational stress scores. The results indicate the importance of time-management skills in reducing occupational stress, but other factors seem to have more effect on reported occupational stress than do the characteristics addressed by this survey.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".