Development, evaluation, and implementation of an online pain assessment training program for staff in rural long-term care facilities: a case series approach
Bibliographic record
Abstract
BACKGROUND: Pain among long-term care (LTC) residents, and especially residents with dementia, is often underassessed and this underassessment has been attributed, in part, to gaps in front-line staff education. Furthermore, although evidence-based clinical guidelines for pain assessment in LTC are available, pain assessment protocols are often inconsistently implemented and, when they are implemented, it is usually within urban LTC facilities located in large metropolitan centers. Implementation science methodologies are needed so that changes in pain assessment practices can be integrated in rural facilities. Thus, our purpose was to evaluate an online pain assessment training program and implement a standardized pain assessment protocol in rural LTC environments. METHODS: During the baseline and implementation periods, we obtained facility-wide pain-related quality indicators from seven rural LTC homes. Prior to implementing the protocol, front-line staff completed the online training program. Front-line staff also completed a set of self-report questionnaires and semi-structured interviews prior to and following completion of the online training program. RESULTS: Results indicated that knowledge about pain assessment significantly increased following completion of the online training program. Implementation of the standardized protocol resulted in more frequent pain assessments on admission and on a weekly basis, although improvements in the timeliness of follow-up assessments for those identified as having moderate to severe pain were not as consistent. Directed content analysis of semi-structured interviews revealed that the online training program and standardized protocol were well-received despite a few barriers to effective implementation. CONCLUSIONS: In conclusion, we demonstrated the feasibility of the remote delivery of an online training program and implementation of a standardized protocol to address the underassessment of pain in rural LTC facilities.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".