Identifying and integrating patient and caregiver perspectives for clinical practice guidelines on the screening and management of infectious microorganisms in hemodialysis units
Bibliographic record
Abstract
INTRODUCTION: The integration of patient and caregiver input into guideline development can help to ensure that clinical care addresses patient expectations, priorities, and needs. We aimed to identify topics and outcomes salient to patients and caregivers for inclusion in the Kidney Health Australia Caring for Australasians with Renal Impairment (KHA-CARI) clinical practice guideline on the screening and management of infectious microorganisms in hemodialysis units. METHODS: A facilitated workshop was conducted with 11 participants (patients [n = 8], caregivers [n = 3]). Participants identified and discussed potential topics for inclusion in the guidelines, which were compared to those developed by the guideline working group. The workshop transcript was thematically analyzed to identify and describe the reasons underpinning their priorities. FINDINGS: Patients and caregivers identified a range of topics already covered by the scope of the proposed guidelines and also suggested additional topics: privacy and confidentiality, psychosocial care during/after disease notification, quality of transportation, psychosocial treatment of patients in isolation, patient/caregiver education and engagement, and patient advocacy. Five themes characterized discussion and underpinned their choices: shock and vulnerability, burden of isolation, fear of infection, respect for privacy and confidentiality, and confusion over procedural inconsistencies. DISCUSSION: Patients and caregivers emphasized the need for guidelines to address patient education and engagement, and the psychosocial implications of communication and provision of care in the context of infectious microorganisms in hemodialysis units. Integrating patient and caregiver perspectives can help to improve the relevance of guidelines to enhance quality of care, patient experiences, and health and psychosocial outcomes.
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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 | 0.044 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".