Multilevel Factors Affecting Quality: Examples From the Cancer Care Continuum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The complex environmental context must be considered as we move forward to improve cancer care and, ultimately, patient and population outcomes. The cancer care continuum represents several care types, each of which includes multiple technical and communication steps and interfaces among patients, providers, and organizations. We use two case scenarios to 1) illustrate the variability, diversity, and interaction of factors from multiple levels that affect care quality and 2) discuss research implications and provide hypothetical examples of multilevel interventions. Each scenario includes a targeted literature review to illustrate contextual influences upon care and sets the stage for theory-informed interventions. The screening case highlights access issues in older women, and the survivorship case illustrates the multiple transition challenges faced by patients, families, and organizations. Example interventions show the potential gains of implementing intervention strategies that work synergistically at multiple levels. While research examining multilevel intervention is a priority, it presents numerous study design, measurement, and analytic challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it