Improving Treatment of Elderly Patients by Interprofessional Education in a Quality Network of Geriatric Medicine: Protocol for Evaluating an Educational Initiative
Bibliographic record
Abstract
BACKGROUND: All statistics on the development of demand for care for multimorbid elderly patients highlight the acute pressure to act to adequately respond to the expected increase in geriatric patient population in the next 15 years. Against this background, great importance must be attached to the improvement of cross-occupational group and cross-sector treatment of these patients. In addition, many professionals in the health care sector often have little knowledge about the special treatment and care needs of the elderly. OBJECTIVE: The Quality Network of Geriatric Medicine in north-west Germany is the body responsible for the project; with its member organizations, it provides care for over 400,000 inpatients and is thus one of the largest associations for geriatrics in Germany. The Quality Network conducts binding evaluated qualification measures for staff involved in the treatment and care of multimorbid elderly patients. The training offers are especially intended for staff who have not yet been trained in working with elderly patients. This approach is intended to improve the expertise of various occupational groups on different hierarchy levels, to include patients and their family members in the evaluation process, and to initiate changes within the organizations. METHODS: Various instruments are used in the evaluation of qualification measures: besides written surveys and questionnaires, structured work groups (consensus groups) and interviews are conducted. The evaluation starts before the qualification measures to determine the starting point and then continues during the measure and after its completion. This allows major findings to be integrated directly into the ongoing qualification program. At least 100 trainings on geriatric topics, 80 consensus groups, and 120 patients (and family members) are going to be included in the study. RESULTS: The evaluation of the educational initiative is funded by the State of Northrhine-Westfalia (Germany; LZG TG 71 001 / 2015 and LZG TG 71 002 / 2015). The results of the study will be published after review and approval by the state authorities - presumably by the end of 2019. The before and after comparison of the treatment-related outcomes at the beginning and near the completion of the educational initiative gives insights into how transfer-oriented education can improve the treatment of elderly patients across sector lines for inpatients as well as outpatients. The evaluation of the implementation of educational content in day-to-day work and occupational groups is to facilitate recommendations about economically sensible use of educational resources and about further adjustments to the training content. CONCLUSIONS: The evaluation develops the foundation for targeted and needs-oriented qualification measures as well as transfer in cross-sector, multiprofessional networks. Instruments and results will be published and provided to other health care networks and institutions. The Quality Network will implement the results of the evaluation process in its member institutions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11067.
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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 | 0.073 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".