Patient-Expressed Perceptions of Wait-Time Causes and Wait-Related Satisfaction
Bibliographic record
Abstract
BACKGROUND: This study set out to identify patterns in the causes of waits and wait-related satisfaction. METHODS: We conducted qualitative interviews with urban, semi-urban, and rural patients (n = 60) to explore their perceptions of the waits they experienced in the detection and treatment of their breast, prostate, lung, or colorectal cancer. We asked participants to describe their experiences from the onset of symptoms to the start of treatment at the cancer clinic and their satisfaction with waits at various intervals. Interview transcripts were coded using a thematic approach. RESULTS: Patients identified five groups of wait-time causes: Patient-related (beliefs, preferences, and non-cancer health issues)Treatment-related (natural consequences of treatment)System-related (the organization or functioning of groups, workforce, institution, or infrastructure in the health care system)Physician-related (a single physician responsible for a specific element in the patient's care)Other causes (disruptions to normal operations of a city or community as a whole) With the limited exception of physician-related absences, the nature of the cause was not linked to overall satisfaction or dissatisfaction with waits. CONCLUSIONS: Causes in themselves do not explain wait-related satisfaction. Further work is needed to explore the underlying reasons for wait-related satisfaction or dissatisfaction. Although our findings shed light on patient experiences with the health system and identify where interventions could help to inform the expectations of patients and the public with respect to wait time, more research is needed to understand wait-related satisfaction among cancer patients.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".