Versorgungsnahe Daten für Versorgungsanalysen – Teil 3 des Manuals
Bibliographic record
Abstract
Analyses of health and health care (hereafter referred to as "health care analyses") usually aim to make transparent the structures, processes, results and interrelationships of health care and to record the degree to which health care systems and their actors have achieved their goals. Health care-related data are an indispensable source of data for many health care analyses. A prerequisite for the examination of a degree of goal achievement is first of all an agreement on those goals that are to be achieved by the system and its substructures, as well as the identification of the determinants of the achievement of the objectives. Primarily it must be examined how safely, effectively and patient-centred systems, facilities and service providers are operating. It also addresses issues of need, accessibility, utilisation, timeliness, appropriateness, patient safety, coordination, continuity, and health economic efficiency and equity of health care. The results of health care include system services (outputs), on the one hand, and results (outcomes), on the other, whereby the results (patient-reported outcomes) and experiences (patient-reported experiences) reported are of particular importance. Health care analyses answer basic questions of health care research: who does what, when, how, why and with which resources and effects in routine health care. Health care analyses thus provide the necessary findings and key figures to further develop health care in order to improve the quality of health care. The applications range from capacity analyses to following innovations up to the concept of regional and supra-regional monitoring of the quality of care given to the population. Given the progress of digitalisation in Health Care, direct data from the care processes will be increasingly available for health care research. This can support care givers significantly if the findings of the studies are applied precisely and correctly within an adequate methodological frame. This can lead to measurable improved health care quality for patients. Data from the process of health care provision have a high potential. Their use needs the same scientific scrutiny as in all other scientific studies.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.179 | 0.188 |
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".