Patient reactions to witnessed medical events in the dialysis center or to the sudden absence of other patients from the center: A qualitative study
Bibliographic record
Abstract
INTRODUCTION: Patients receiving in-center maintenance hemodialysis (HD) spend a significant amount of time together. To date, little or no research has examined how these patients perceive and process other patients' medical events in and absences from their centers. We therefore undertook this qualitative study using semi-structured interviews to explore these phenomena from the patient perspective. METHODS: Patients at a suburban Pittsburgh HD center participated in semi-structured interviews in April to May 2011, reporting on their impressions of their relationships with other patients in the center; their experiences of witnessing clinical decompensations in the center; and their reactions to absences of fellow patients from the center. Trained coders developed a codebook and applied it to interview transcripts. FINDINGS: There were 17 participants, 47% women, 29% black, with median age 63 years. Almost every participant had witnessed other patients' medical events during HD. Three main themes emerged in analysis of interviews: (1) incomplete knowledge of many aspects of witnessed events and patient absences in the HD center; (2) a process of "filling in the blanks": Participants used their own past events and absences to help process other patients' events and absences and used other patients' events and absences to help process their own future events and absences; and (3) participants' broad support for HD center staff being able to share with other patients basic information about their whereabouts if they themselves are absent from the center. DISCUSSION: Witnessed medical events in and patients' absences from the HD center are not only common but are also important to patients, who struggle to process these events and absences due to limited information about what actually happened. Interventions, such as providing patients with more information, could improve patients' experience of witnessed events and fellow patients' absences and potentially impact other patient-centered 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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".