Trends in oral and maxillofacial radiology career: A survey
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To understand the professional aspirations of oral and maxillofacial radiologists (OMRs) by exploring their career choices and their association with educational background, engagement in professional activities, professional values, and overall level of career satisfaction. METHODS: This prospective cross-sectional study surveyed board-certified OMRs in the United States and Canada from September to December 2019. A web-based questionnaire was created comprising 37 multiple-choice questions and an open-ended question focusing on their opinions regarding the profession's future. A thematic approach qualitatively explored open questions. RESULTS: Of the 86 OMRs, 68, 10, and eight were board certified in the United States, Canada, and both countries, respectively. Activities considered "rewarding" included teaching and mentoring (65%) and radiologic reporting (55%). The majority spent approximately 20-30 h/week writing radiographic reports and less than 10 h in research. On an average, OMRs produced 21.9 (SD 12.8) reports per day. Activities considered less rewarding included administrative work (11%) and productivity pressure in institutions. OMRs working in academia reported higher incomes (p < 0.05). Finally, the majority of the OMRs were pleased with their career choices (79%). CONCLUSIONS: There is an association between the contemporary OMRs' satisfaction feeling, teaching/mentoring, and the future challenges of participating in multidisciplinary teams. Overall, diverse career choices lead OMRs to be proud of their profession and significantly satisfied.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".