Improving Quality of Service of Home Healthcare Units with Health Information Technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deployment of health information technologies (HITs) provides home care units with the means to generate improvements in accuracy and timeliness of information required to meet dynamic patient demands and provide high quality patient care. Increasing availability of information can also facilitate organisational learning, which leads to the invocation of processes that result in improved responses and decisions. This study examined crucial links between HITs and quality of service provided through an empirical investigation of 252 patients in a hospital-in-the-home unit (HHU) in a Spanish regional hospital. The study sought to test the relationship between HITs and the quality of service using factor analysis and structural equation modeling (SEM) to investigate how HITs mediate effects of organisational learning on quality of service. Findings support the notion that the relationship between organisational learning and quality of service can be mediated by HITs. This study provides HHU managers with guidelines for understanding the role of organisational learning processes with respect to HITs and quality of service.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| 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 it