Implementing ‘self-help friendliness’ in German hospitals: a longitudinal study
Bibliographic record
Abstract
In Germany, the term 'self-help friendliness' (SHF) describes a strategy to institutionalize co-operation of healthcare institutions with mutual aid or self-help groups of chronically ill patients. After a short explanation of the SHF concept and its development, we will present findings from a longitudinal study on the implementation of SHF in three German hospitals. Specifically, we wanted to know (i) to what degree SHF had been put into practice after the initial development phase in the pilot hospitals, (ii) whether it was possible to maintain the level of implementation of SHF in the course of at least 1 year and (iii) which opinions exist about the inclusion of SHF criteria in quality management systems. With only minor restrictions, the findings provide support for the usefulness, practicability, sustainability and transferability of SHF. Limitations of our empirical study are the small number of hospitals, the above average motivation of their staff, the small response rate in the staff-survey and the inability to get enough data from members of self-help groups. The research instrument for measuring SHF was adequate and fulfils the most important scientific quality criteria in a German context. We conclude that the implementation of SHF leads to more patient-centredness in healthcare institutions and thus improves satisfaction, self-management, coping and health literacy of patients. SHF is considered as an adequate approach for reorienting healthcare institutions in the sense of the Ottawa Charta, and particularly suitable for health promoting hospitals.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".