Factors Associated With Missed Nursing Care in Home Care Setting: Insights From the AIDOMUS‐IT Multicentre Study
Bibliographic record
Abstract
Aims: To explore factors associated with missed nursing care (MNC) in home care in Italy. Methods: A secondary analysis of the AIDOMUS‐IT national cross‐sectional study was conducted investigating structural factors, including details on services offered, waiting times, nurses’ working conditions and workload, nurses’ perceptions of the work environment, climate, staffing adequateness, opportunities for career advancements, leadership, level of burnout, and work‐life balance. Nurses’ and patients’ characteristics were hypothesized as “part of the MNC process,” while patients’ perception of care as an MNC outcome. The “Missed Nursing Care in the Home Care” (MNC_HC) instrument was developed and validated. Other instruments used were the “Practice Environment Scale of the Nursing Work Index,” the “NASA Task Load Index,” and the “Copenhagen Psychosocial Questionnaire version III”. Data from nursing directors, home care nurses, and patients were used in a quantile regression to explore factors linked to MNC. A univariate linear regression model assessed the relationship between MNC and patients’ evaluation of the service. Results: A total of 3949 nurses and 9780 patients participated in this study. MNC was reported by 3545 nurses (89.77%), and MNC_HC mean score of items of care missed was 5.23 (SD = 3.18) out of 9. When MNC was low, overtime work increased it, while staffing adequacy and leadership quality reduced it. When MNC was at a medium level, associated factors included longer patient waiting times, more home visits per shift, and inadequate staffing. When MNC was high, work‐life conflict and burnout were strongly associated with increased MNC. High perceived workload and lack of career progression opportunities were consistently associated with MNC, regardless of its level. Conclusion: A critical appraisal of organizational and staffing features is recommended in home care. To enhance both patient outcomes and nurse satisfaction, it is advisable to implement indicators to monitor care delivery, revise nurse staffing levels, and establish advanced roles, such as specialized community nursing positions.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".