Exploring Nurse and Patient Experiences of Developing Rapport During Oncology Ambulatory Care Videoconferencing Visits: Protocol for a Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Telehealth videoconferencing has largely been embraced by health care providers and patients during the COVID-19 pandemic; however, little is known about specific techniques for building rapport and provider-patient relationships in this care environment. Although research suggests that videoconferencing is feasible and can be effective for some types of care, concerns about the impact of technology on provider-patient relationships exist across health disciplines. Suggestions for adapting some in-person rapport techniques, such as the use of small talk, eye contact, and body language to facilitate trust, personal connection, and communication during videoconferencing encounters, have been discussed in the popular press and clinical commentaries. Notably, evidence regarding the effects of these strategies on rapport and clinical care outcomes is lacking. Understanding how to establish rapport in videoconferencing visits is especially important in oncology nursing, where rapport with patients enables nurses to become a source of emotional support, helping patients adapt and navigate the cancer journey. OBJECTIVE: This study aims to investigate the nature of nurse-patient rapport in ambulatory cancer care videoconferencing visits. The objectives include exploring how patients with cancer and nurses describe experiences of rapport and strategies for cultivating rapport in videoconferencing visits and similarities and differences identified by patients with cancer and nurses between experiences of rapport in videoconferencing and in-person visits. METHODS: Semistructured narrative interviews of patients with cancer and nurses will be conducted to understand the experience of rapport building in videoconferencing visits. Nurses and patients will be interviewed separately to facilitate an understanding of the perspectives of both types of participants. Interviews will be conducted on a secure videoconferencing platform. This qualitative descriptive study will describe participant experiences in a manner that, although not without interpretation, is as close to the data as possible. The research team will meet regularly to discuss, define, and document codes, categories, and themes, and the team will maintain a detailed audit trail of analytical decisions. In addition, member checking will enhance the rigor of the study. Nurse and patient interviews will be analyzed separately using identical procedures and may be explored side by side in the final analysis to provide a comparative analysis. Data management and analysis will be performed using NVivo 12. RESULTS: Data collection will begin during summer 2021, with results from the data analysis anticipated by winter 2021. A research team trained in qualitative methodology will use conventional content analysis to analyze the data using first- and second-level codes derived directly from the transcribed text data. CONCLUSIONS: This study aims to determine what behaviors, communication techniques, and relational practices need to be adapted in videoconferencing telehealth visits, setting the foundation for future development of interventions and evidence-based practice guidelines for relationship building during videoconferencing telehealth visits. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/27940.
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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 | 0.058 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".