Exploring Oral Care in Long‐Term Care Homes: An Institutional Ethnography
Bibliographic record
Abstract
AIMS: Oral health of long-term care (LTC) residents remains a concern despite many years of evidence-based interventions. This study aimed to explore the social organization of care contributing to poor oral health of residents in LTC homes. MATERIALS AND METHODS: Institutional ethnography (IE) was used from the healthcare aide (HCA) standpoint. The oral care provided by HCAs in the LTC homes was explored through observations and interviews that focused on HCAs' interactions with institutional texts. Data analysis included text-work-text (TWT) mappings to illustrate how oral care was socially organized. RESULTS: HCAs determined how and when to provide oral care for residents. Standardized assessments designed by the regulatory health authority to assist LTC homes in complying with care standards were insufficiently detailed to guide the oral care provided by HCAs. HCAs lacked knowledge about oral care beyond tooth-brushing and denture care. CONCLUSIONS: In the absence of effective institutional texts, HCAs used their discretion, relying on their personal experiences, to inform the oral care provided for residents. This study helps to understand the organizational factors that contribute to poor oral health and offers alternative intervention strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".