The impact of oral health education taken by personal support workers caring for a geriatric population in a long‐term care facility: A mixed‐methods study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of an online oral health education module on personal support workers' (PSW) knowledge and beliefs in their care for long-term care (LTC) residents in one Canadian LTC residence. BACKGROUND: LTC residents are dependent on PSWs for their oral health needs. However, PSWs receive minimal to no oral health education placing residents at risk for poor oral health. METHODS: A mixed-methods convergent design comprising a before-and-after questionnaire (N = 88), focus groups (N = 23) and interviews (N = 4) exploring module learning. Analysis of each data set was followed by their amalgamation and comparison. RESULTS: The online module had limited impact on the PSWs' knowledge and beliefs regarding resident oral health care. The quantitative results demonstrated knowledge improvements in two domains and changes in two belief domains. However, the qualitative results demonstrated new knowledge was not developed or sustained in practice. Themes that emerged include the following: lack of module recall, unmet learning needs and methods for oral care delivery, and timing of oral care in a busy clinical environment. CONCLUSION: Online oral health education alone has limited impact on PSWs' knowledge and beliefs. Research evaluating multifaceted education interventions including hands-on training with a dental expert is warranted.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".