How access to online health information affects the dental hygiene client experience.
Bibliographic record
Abstract
Objective: Due to the widespread availability of online information, oral care providers are no longer the main source of oral health information for clients. This shift in the balance of knowledge has the potential to alter clients' experiences and relationships with their oral care providers, including dental hygienists. This review explores how access to online health information has influenced clients' experiences with their dental hygienists. Method: , PubMed, and CINAHL. Twenty-three studies published between 2005 and 2020 were included. Results and discussion: The majority of clients used the internet to access health information to be better informed about health issues. Both clients and health care providers had concerns about the legitimacy and accuracy of various online information sources. Clients faced various communication facilitators and barriers when discussing this information with their health care provider. A positive response by the health care provider led to an improved client-clinician relationship, whereas a negative response led to distrust among all parties. Clients would be open to e-health literacy training by their dental hygienists. Conclusion: Clients' access to online health information can either improve or worsen their experiences and relationships with their dental hygienists, depending on the response by the dental hygienist when these topics are broached. Dental hygienists should consider taking time to provide e-health literacy training to clients during consultations.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".