Using Mixed Methods for Evaluating an Integrative Approach to Cancer Care: A Case Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of participating in an integrative cancer care program at the Centre for Integrated Healing in Vancouver, British Columbia, on patients' lifestyle, quality of life, and overall well-being. STUDY DESIGN: A mixed-methods case study with a pre- and posttest design. No control group was utilized. METHODS: All new patients starting at the Centre for Integrated Healing between May and September of 2004 were invited to join the study. Forty-six of 77 new patients agreed to participate. Quantitative data measuring quality of life, social support, anxiety and depression, locus of control, and hope were assessed at baseline (pre-program start) and at 6 weeks and 5 months from the start of the program. Qualitative data in the form of focus groups and interviews were collected midway through the follow-up period to further explore program impacts. RESULTS: No statistically significant improvements or declines were noted on the quantitative measures between baseline and the 5-month follow-up point. The qualitative findings revealed a theme of patients' active engagement in their cancer care involving empowered decision making and creating personal change. Facilitators of active patient engagement in their own care from the integrative program included healing partnerships with practitioners, information and resources, managing the integration of complementary and conventional therapies, emotional support, and a sense of hope. DISCUSSION: This case study was a first attempt at documenting the impact of an integrative cancer care program at the Centre for Integrated Healing. Study limitations included a small sample size, which limited power to detect quantitative changes on the questionnaires and a lack of a control group. Qualitative findings indicated that patients found value in the "person-oriented" holistic approach to care, which encouraged patients to take an active role in decision making and self-care. The use of a mixed-methods research design proved to be an effective approach to not only evaluating outcomes but also examining process issues of the experience. Additional research is greatly needed to better understand potential impacts of integrative approaches to cancer care.
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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 | 0.120 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".