If we cannot measure it, we cannot improve it: Understanding measurement problems in routine oral/dental assessments in Canadian nursing homes—Part II
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Objective To evaluate the response process validity of the Resident Assessment Instrument—Minimum Data Set 2.0 (RAI) oral/dental items and the organisational processes for assessing nursing home (NH) residents’ oral/dental status. Background Although care aides provide most direct care to NH residents, including oral care, they are not directly involved in structured care planning activities, including RAI assessments. This most likely affects the accuracy of RAI assessments, as well quality of care. However, we neither know how well regulated and unregulated care staff understand the RAI oral/dental items, nor what processes are used in completing oral/dental assessments. Methods We conducted nine focus groups with 44 care aides, nurses, allied health providers, clinical specialists and managers. We discussed randomly selected RAI oral/dental assessments with focus group participants, including participants’ understanding of the items and why the options were selected. Participants also explained the communication and process for completing the RAI. Results Participants’ perceptions of the oral/dental items aligned fairly well with the item definitions. However, responses primarily focused on severe oral/dental problems with obvious physical characteristics (eg black teeth denoting caries). For non‐visual oral problems, such as pain, staff relied on resident verbalisation. No formal mechanisms were described for care aides to update nurses on residents’ oral health needs. Conclusions Performance problems of RAI oral/dental items are largely rooted in poor communication between care aides and nurses and not integrating care aides in assessment processes. We need policies that address these problems in order to improve NH residents’ poor oral health.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it