Integrative Health Education For Insomnia Among The Older Adult Population
Bibliographic record
Abstract
Purpose This quality improvement (QI) project aims to evaluate the healthcare providers’ knowledge and intent to use evidenced-based integrative health (IH) education plan for insomnia in older adults at the Cornerstone Long-term Acute Care Hospital in Round Rock, TX. Background The American College of Physicians Clinical Practice Guideline (CPG) for chronic insomnia for the adult population contains two recommendations, the use of cognitive-behavioral therapy and a shared-decision-making approach by the clinician and the patient discussing whether pharmacological intervention is warranted in case the CBT was ineffective. This project identifies alternative nonpharmacological approaches in a situation where both the first and second recommendations are not effective, appropriate, or the risks outweighs the benefits. Methods The Ottawa Model of Research Use (OMRU) framework inspired by Rogers Diffusion of Innovation theory guided the process of this quality improvement project, which superseded a quantitative descriptive design. This design was utilized to evaluate the healthcare providers’ knowledge of evidence-based integrative health nonpharmacological interventions for insomnia. A Google.doc pretest survey link was provided followed by a PowerPoint education presentation about the history, uses, and benefits of acupuncture, aromatherapy, and music therapy for insomnia. A posttest survey link was provided after the presentation to re-assess their knowledge and intent to use IH approaches to their practice. The pretest and posttest survey results were used as a piece of quantifiable information for analysis. Results Data collection took place in one evening. Two participants participated in this project presentation, all of which were Caucasian female, registered nurses. Both completed the pretest and posttest surveys. There were score increases seen in knowledge, comfortability, and intent to use acupuncture, aromatherapy, and music therapy as IH approaches to their practice and a clear improvement from pretest survey to posttest survey. Conclusion The pretest and posttest surveys revealed positive results of evidence-based IH approaches education presentation to the healthcare providers’ knowledge and intent to use to their practice. Future high-quality studies that focus on adapting the IH approaches into the providers’ practice would likely lead to the conceptualization of the 3rd recommendation of the existing CPG for insomnia among adults
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".