Qualitative Exploration of Healthcare Relationships Following Delayed Diagnosis of Ovarian Cancer and Subsequent Participation in Supportive-Expressive Group Therapy
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To explore the role of supportive-expressive group therapy (SEGT) in facilitating the development and quality of healthcare relationships in patients with ovarian cancer. RESEARCH APPROACH: Qualitative, grounded theory, and comparative approach. SETTING: Tertiary care cancer center. SAMPLE: 6 patients with advanced ovarian cancer and 3 healthcare professionals. METHODOLOGIC APPROACH: Patients participated in semistructured interviews that examined the nature of their healthcare relationships, diagnoses, and SEGT experience. The primary gynecologic oncologist and two nurses responsible for the care of the patients also were interviewed. Analysis of this qualitative study employed a grounded theory technique. MAIN RESEARCH VARIABLES: Patients' and healthcare professionals' perceptions of healthcare relationships. FINDINGS: Patients' negative diagnostic experiences were found to influence the quality of relationships with healthcare providers. However, the process appears to benefit from patient participation in SEGT. Patients perceived that SEGT helped facilitate communication between patients and professionals. Patients also indicated that SEGT led them to participate more actively in the treatment process. Professionals viewed patient participation in SEGT as a positive outlet for emotional expression, a source of psychological healing, and a tool that facilitated communication, collaboration, and understanding of medical treatment. CONCLUSIONS: Participation in SEGT can advance communication and collaboration in medical care and provide opportunity and resources for psychological healing. INTERPRETATION: SEGT provides a vehicle to enhance the quality of life of patients with ovarian cancer by breaking down the common feeling of isolation, addressing women's frustration and resentment regarding delayed diagnosis, and enhancing relationships with healthcare providers to promote collaborative care in this patient population.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".