Does a personalized multifaceted care plan (PMCP) improve quality of care?
Bibliographic record
Abstract
e17663 Background: Breast cancer (BC) is the most frequently diagnosed cancer in women in North America. Institute of Medicine (2005) recommended that principal providers of oncology treatment provide patients with a care plan. Our earlier study showed that “one size does not fit all” and care plans need to be personalized (Haq et al., 2013). In this study, a Personalized Multifaceted Care Plan (PMCP) was developed and evaluated. Methods: A mixed methods design was used in this randomized controlled study. A total of 72 women diagnosed with invasive BC and expected to undergo neoadjuvant/adjuvant treatments were recruited from the multidisciplinary breast clinic. Participants were randomized to the control or intervention group, and stratified to chemotherapy versus endocrine therapy. The PMCP(intervention) included patients’ breast pathology, recommended treatment and follow-up care plans, and access to the study website. Quantitative outcomes were assessed through quality of life (Edmonton Symptom Assessment System-revised) and quality of care (Patient Satisfaction with Cancer Care, and Communication and Attitudinal Self Efficacy scale for Cancer) questionnaires that patients completed before and after chemotherapy treatment. Baseline and post-intervention questionnaire scores are compared among the control and intervention groups, and across treatments using unpaired t-tests. After completion of treatment, qualitative analysis was conducted in the intervention group, using patient focus groups and primary care provider (PCP) interviews. Results: Qualitative: 17 of the 18 focus group participants found that the PMCP helped them feel ‘educated’ about their cancer treatment. As one person described, “knowledge is power” and the PMCP “changes the level of knowledge and involvement you feel you have”. PCPs found the PMCP contained useful, concise medical summaries. Quantitative: Post-intervention quality of care scores increased in the intervention group but was unchanged in the control group. Further findings will be presented at the conference. Conclusions: PMCP appears to improve quality of care. Qualitative findings indicate the PMCP provide useful information to educate patients and assist PCPs in caring for them.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".