Complaints concerning communication reported by users of healthcare in a specific region in Sweden
Bibliographic record
Abstract
Introduction: Effective communication between patients and healthcare staff is important in all healthcare services. Previousstudies investigating criticism and complaints concerning treatment reported by patients and relatives in a healthcare context pointto the most common complaints were unsatisfactory information, unsatisfactory respect and unsatisfactory empathy, but furtherinvestigation is needed. Objective: The aim of this study was to explore complaints reported by patients and relatives in a countycouncil area in the context of communication between patients and healthcare staff, and to investigate the impact complaints canhave on the safety and quality of healthcare.Methods: An exploratory descriptive design was used with a participatory approach. 115 complaints from patients and relatives,collected from various contexts relating to healthcare, were analyzed through qualitative content analysis.Results: Four categories emerged from the analysis of complaints: 1) inadequate communication; 2) inadequate individualisticand holistic healthcare; 3) unprofessional attitude of healthcare staff; and 4) the complaints had both a negative and positiveimpact on the organization of healthcare. The study showed that complaints were related to a lack of adequate verbal and writtencommunication, the patients’ feelings that the healthcare staff did not taking their experiences seriously, and an unprofessional,indifferent and discriminatory attitude among the healthcare staff. The complaints had both a negative and positive impact on theorganization of healthcare.Conclusions: This study highlights how it is possible to learn from complaints about healthcare, and demonstrates that this is aprerequisite for improving healthcare practice. Knowledge about where healthcare practice is failing can be increased, and thiscan be fed into policies for patient safety and quality healthcare.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".