Experiences of dentists and caregivers of patients with special care needs–A qualitative study
Bibliographic record
Abstract
BACKGROUND: Patients with special care needs (developmental disabilities) have unique and complex needs regarding their oral health and care. This qualitative study aimed to identify the experiences, preferences and challenges of dentists and caregivers regarding behavior guidance techniques for dental care in persons with special care needs. METHODS: Relying on qualitative description as articulated by Sandelowski, we conducted telephone interviews with a purposeful sample of five special care dentists and seven caregivers. We analyzed the data using thematic analysis. RESULTS: Four themes were highlighted: (1) Neither pharmacological or non-pharmacological behavior guidance techniques was universally suitable, (2) A patient-centered approach was critical, (3) The dental environment triggered patients' behaviors and anxiety levels, (4) There was more demand for, than supply of, qualified dentists to treat patients with special care needs. CONCLUSIONS: Persons with special care needs are heterogeneous and respond to various behavioral techniques required to deliver their treatment. Behavior guidance planning should be negotiated carefully with patients and caregivers and then individualized based on patients' capabilities and needs for treatment. The necessity to manage complex behaviors has contributed to the limitation of access to dental care for persons with special care needs. Dentistry as a profession has the obligation to uphold the social contract and meet its responsibility to the dental care needs of this population.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".