Connecting the voices of users, caregivers and providers on service quality
Bibliographic record
Abstract
PURPOSE: This article aims to discuss the relevancy of different instruments used to gather information on homecare service quality from multiple stakeholders and the challenges encountered when trying to blend their views for prioritizing areas needing improvement. DESIGN/METHODOLOGY/APPROACH: The study centers on four homecare agencies: one public, one private for-profit and two not-for-profit services, implementing continuous quality improvement (CQI) programs. Various instruments were tested with random and convenience elderly service user, family caregiver and front-line worker samples. Instrument evaluation included operational effectiveness and agency manageability. FINDINGS: A qualitative approach, centered on small stakeholder samples, is fairly effective at assessing service quality, yet demands a strong commitment from agencies in personnel time and resources, as well as the necessary skills. Small-size, private homecare providers seem less-well equipped to handle comprehensive assessments without external support More importantly, assessments have to be done strategically, such that timing and work needed does not undermine program viability. PRACTICAL IMPLICATIONS: The approach and instruments tested have practical implications for decision makers and homecare organization managers interested in CQI. ORIGINALITY/VALUE: The article systematically evaluates quality assessment and priority-setting instruments applied to various stakeholders and homecare settings.
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.
How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".