Dentists' perceptions of providing care in long-term care facilities.
Bibliographic record
Abstract
AIMS: To compare the perceptions of dentists in British Columbia regarding their decisions to provide treatment in long-term care facilities and to explore changes since 1985 in Vancouver dentists' attitudes to treating elderly patients in such facilities. MATERIALS AND METHODS: Dentists were randomly selected from all of British Columbia in 2008 and surveyed with a similar questionnaire to that used for a 1985 study of Vancouver dentists. The attitudes of current dentists, the patterns of their perceptions and trends over time were analyzed. RESULTS: Of the 800 BC dentists approached for the survey in 2008, 251 replied (31% response rate). Only 37 (15%) of these respondents were providing treatment in long-term care facilities, and another 48 (19%) had stopped providing services in this setting. Among those providing care, important considerations were continuing education in geriatrics, the presence of a dental team and fee-for-service payment. The most common reasons for deciding to provide services in long-term care facilities were to increase the number of patients being served and to broaden clinical practice. Dentists who had stopped treating patients in long-term care facilities reported their perception that treating elderly people is financially unrewarding and professionally unsatisfying. The perceptions of dentists shifted substantially from 1985 to 2008. In particular, dentists responding to the 2008 survey who had never provided services in long-term care facilities were more likely to perceive administrative difficulties and a lack of financial reward as barriers than those surveyed in 1985. In addition, the proportion of Vancouver dentists with advanced education in geriatrics declined over the period between the 2 studies (75 [22%] of 334 in 1985, 10 [11%] of 87 in 2008). CONCLUSION: Dentists who did not provide care for residents of long-term care facilities in 2008 seemed more likely to be deterred by administrative difficulties and financial costs than those not providing such care in 1985. In addition, fewer dentists had appropriate training in geriatrics. Continuing education, working with a dental team and payment on a fee-for-service basis were important factors for dentists who were providing care in such facilities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".