Barriers to care in two differing models of follow-up for long-term childhood cancer survivors.
Bibliographic record
Abstract
100 Background: Long-term childhood cancer survivors (CCSs) require lifetime medical follow-up to screen for and treat adverse health outcomes related to cancer and its treatment. In Alberta, primary-care providers (PCPs) are involved in this follow-up care, though differently so in Northern and Southern regions of the province. Our aim was to identify potential province-wide and region-specific preferences and care barriers that may impact medical and quality-of-life outcomes for patients. Methods: Forty-four PCPs following CCSs completed surveys about knowledge gaps, communication, and health-system barriers. Knowledge gaps were assessed using a hypothetical case vignette followed by three multiple-choice questions regarding appropriate follow-up, based on available guidelines. Communication barriers were measured using a 6-item 5-point scale. Health-system barriers were measured using a 12-item 5-point scale. Results: PCPs were 54.54% female, age 31-72 ( m=51.36) with 1-50 years of practice ( m=22.49). These preliminary results suggest PCPs across Alberta are experiencing moderate communication barriers (scale: 0-24, m=10.85, SD=6.59) but fewer health-system barriers (scale: 0-48, m=14.58, SD=5.11). Scores on the case vignette were markedly low province-wide (scale: 0-3, m=0.65, SD=0.65), indicating considerable knowledge gaps regarding long-term oncology-related care follow-up. No statistically significant differences were observed between regions. Conclusions: Albertan PCPs are experiencing barriers to care and knowledge gaps when following ASCCs. This is of primary concern for PCPs in Southern Alberta, currently tasked with the sole responsibility of providing oncology-related follow-up care. These data will be linked with data collected from their patients to explore the relationship between PCPs’ barriers and knowledge, and the symptoms, quality of life, and unmet needs of their 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".