Applying Implementation Science to Identify Primary Care Providers’ Enablers and Barriers to Using Survivorship Care Plans
Bibliographic record
Abstract
Primary care providers (PCPs) have been given the responsibility of managing the follow-up care of low-risk cancer survivors after they are discharged from the oncology center. Survivorship Care Plans (SCPs) were developed to facilitate this transition, but research indicates inconsistencies in how they are implemented. A detailed examination of enablers and barriers that influence their use by PCPs is needed to understand how to improve SCPs and ultimately facilitate cancer survivors' transition to primary care. An interview guide was developed based on the second version of the Theoretical Domains Framework (TDF-2). PCPs participated in semi-structured interviews. Qualitative content analysis was used to develop a codebook to code text into each of the 14 TDF-2 domains. Thematic analysis was also used to generate themes and subthemes. Thirteen PCPs completed the interview and identified the following barriers to SCP use: unfamiliarity with the side effects of cancer treatment (Knowledge), lack of clarity on the roles of different healthcare professionals (Social Professional Role and Identity), follow-up tasks being outside of scope of practice (Social Professional Role and Identity), increased workload, lack of options for psychosocial support for survivors, managing different electronic medical records systems, logistical issues with liaising with oncology (Environmental Context and Resources), and patient factors (Social Influences). PCPs value the information provided in SCPs and found the follow-up guidance provided to be most helpful. However, SCP use could be improved through streamlining methods of communication and collaboration between oncology centres and community-based primary care settings.
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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 | 0.107 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".