[Is there a tendency for professional saturation in orthodontics in Israel?].
Bibliographic record
Abstract
OBJECTIVES: The purpose of the present study is to evaluate the demand for orthodontic treatment in Israeli society, and determine the adequacy of the profession to provide service. In addition, a database of Israeli orthodontic specialist demographics will be compiled. METHODS: There is no statistical information available as to the orthodontic treatment needs and demands in Israel. Furthermore, no survey exists measuring response to orthodontics by the Israeli patient population (i.e. attitudes, treatment time etc). In order to gauge these parameters, a written questionnaire was distributed to all the orthodontists in Israel (specialists, post graduate students and dentists who have completed their orthodontic training but have not yet earned specialist certification). The survey contained questions regarding work hours (full or partial time), the numbers of orthodontic care facilities and the distance travelled to each office, the orthodontist's claim of "free time", and whether there is a desire to increase the time spent treating patients. RESULTS: A total of 89 orthodontists complied with the conditions of the study. Sixty nine (77.5%) were male, and 65 (73%) of them were certified specialists. It was found that 9% of the responding orthodontists practiced general dentistry in addition to orthodontics during at least 25% of their clinic time. A majority of the orthodontists (60/89) work in more than one office and 27% work in four or more different offices. About a quarter (25.8%) of the responding orthodontists report having less work than they desire and 16.9% of the orthodontists would like to work 10 or more additional hours per week. The majority of the orthodontists (80.9%) live in the central part of Israel and they travel long distances to work. Almost half of the offices (47.6%) are located 30 km or more away from their homes. CONCLUSIONS: The present survey indicates that the demographics within the orthodontic specialty tend towards that of professional over-supply (saturation). It was also found that the majority of the orthodontists live in the central region of Israel, therefore, travelling to satellite offices is inherently time consuming.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".