Primary care providers’ evaluation of survivorship care plans.
Bibliographic record
Abstract
188 Background: Survivorship care represents a distinct phase of the cancer care and addresses surveillance, the physical and psychosocial sequelae of the cancer and treatment, and health promotion. Survivorship care plans (SCP) were recommended in the 2005 IOM From Cancer Patient to Cancer Survivor as a tool to address these issues and will be required by the Commission on Cancer in 2015. As part of two studies implementing SCP using the JourneyForward (JF-SCP) template, we evaluated primary care providers (PCP) perspective on their use. Methods: JF-SCP were developed and delivered to patients and PCP at the end of treatment. We collected information about usability, comprehension, barriers to use, and respondent demographics. Results: Of the 61 PCP who responded: 86% were physicians, 60% were women, had an average of 20 years in practice. PCP strongly agreed/agreed that SCP were easy to understand (95.7%), the right length (73.9%), addressed the right topics (95.7%), would be useful in talking with patients about survivorship plans (91.3%), would improve communication with oncologists (74.4%) and with patients (74.4%). Significant barriers interfering with the PCP providing follow-up care included: limited access to survivors as they stay with oncologists (59.5%), insufficient knowledge of issues (53.4%), inadequate recommendations from the oncologist (43.9%), lack of survivor care guidelines (42%), lack of time (38%), and poor reimbursement (16.6%). Specific suggestions about the JF-SCP included making it shorter and being clear about which provider was responsible for obtaining recommended testing. PCP commented on the importance of ongoing primary care provided throughout the cancer continuum. Conclusions: PCP reported ease of SCP use but did identify areas for improvement. Many PCP were frequently not included or did not stay involved with the cancer patient when being seen by oncologists. The SCP was viewed as a tool to facilitate communication with oncologists and 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 | 0.018 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".