Bibliographic record
Abstract
OBJECTIVE: To provide an overview of the literature about uncertainty in health care and how it relates to the oral health care of older people. BACKGROUND: The medical literature describes uncertainty in health care from the initial informed-consent to its impact on a patient's ability to cope with undesirable outcomes. METHODS: A narrative review of the medical, dental and psychological literature was conducted to identify pertinent information on the theory and implications of uncertainty in healthcare. The findings are infused into a case-report illustrating the recurrence of uncertainty experienced by an older woman who had multiple treatments over several years to restore her dentition damaged severely by dental caries. RESULTS: Uncertainty originates from inadequate understanding, incomplete information and undifferentiated alternatives leading to unnecessary diagnostic tests and healthcare costs. A conceptual taxonomy clarifies the characteristics of uncertainty in the context of scientific, practical or personal sources and offers management possibilities through effective communications to identify choices and probabilities that help patients to adapt and cope with adverse events. CONCLUSIONS: Uncertainty pervades healthcare. It can lead patients to self-blame, anger and withdrawal from care unless it is communicated effectively so that they can adapt and cope with the disappointment of adversity and continue with alternative approaches to care.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".