Narrative Reflective Process: A Means to Whole Person Research Cluster Formation
Bibliographic record
Abstract
Objectives: As nursing faculty members of Health and Illness research cluster our focus is on promoting humanistic practices, such as person-centered care, for individuals and their families living with illness and its consequences. In keeping with this holistic focus we wanted to explore a non-traditional approach to building a productive professional collaboration.Methods: In the early stages of our research cluster formation, we chose to engage in Schwind’s Narrative Reflective Process (NRP), a creative self-expression activity that is informed by the Connelly and Clandinin’s Narrative Inquiry research approach. Creative activities included storytelling, metaphors and drawing. We began the process by working in pairs, telling stories of our research experiences, both positive and negative. We then drew images that metaphorically represented each of these. Following, we described our images to the cluster group, while considering common narrative threads. The process ended by the group envisioning our interpretation of our collaborative cluster work. Throughout the year, our cluster met on a regular basis working on our common interest of person-centred care. One year later, we recreated our images to see how the view of our collaborative cluster work had evolved. This time we chose to create one large image together.Results: We found that our creative experiential activities opened possibilities for trust and created a safe space for research cluster members to share their developing ideas. We felt that by sharing our personal experiences through drawings and metaphors allowed us to connect with one another as whole persons. It allowed us to recognize that we are more than just our professional roles.Conclusions: Through the creative self-expression of NRP we engaged the research cluster members as whole persons. As a result, the cohesion, trust and collaboration resulted in increased research creativity and productivity, which would not have been possible to create alone.
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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 | 0.090 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".