Exploring transitions in care among patients with head and neck CANCER: a multimethod study
Bibliographic record
Abstract
BACKGROUND: Patients with head and neck cancers (HNC) experience many transitions in care (TiC), occurring when patients are transferred between healthcare providers and/or settings. TiC can compromise patient safety, decrease patient satisfaction, and increase healthcare costs. The evidence around TiC among patients with HNC is sparse. The objective of this study was to improve our understanding of TiC among patients with HNC to identify ways to improve care. METHODS: This multimethod study consisted of two phases: Phase I (retrospective population-based cohort study) characterized the number and type of TiC that patients with HNC experienced using deterministically linked, population-based administrative health data in Alberta, Canada (January 1, 2012, to September 1, 2020), and Phase II (qualitative descriptive study) used semi-structured interviews to explore the lived experiences of patients with HNC and their healthcare providers during TiC. RESULTS: There were 3,752 patients with HNC; most were male (70.8%) with a mean age at diagnosis of 63.3 years (SD 13.1). Patients underwent an average of 1.6 (SD 0.7) treatments, commonly transitioning from surgery to radiotherapy (21.2%). Many patients with HNC were admitted to the hospital during the study period, averaging 3.3 (SD 3.0) hospital admissions and 7.8 (SD 12.6) emergency department visits per patient over the study period. Visits to healthcare providers were also frequent, with the highest number of physician visits being to general practitioners (average = 70.51 per patient). Analysis of sixteen semi-structured interviews (ten patients with HNC and six healthcare providers) revealed three themes: (1) Navigating the healthcare system including challenges with the complexity of HNC care amongst healthcare system pressures, (2) Relational head and neck cancer care which encompasses patient expectations and relationships, and (3) System and individual impact of transitions in care. CONCLUSIONS: This study identified challenges faced by both patients with HNC and their healthcare providers amidst the frequent TiC within cancer care, which was perceived to have an impact on quality of care. These findings provide crucial insights that can inform and guide future research or the development of health interventions aiming to improve the quality of TiC within this patient population.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".