Learning from reflective journaling; the experience of navigators in assisting patients access to health and social resources in the community
Bibliographic record
Abstract
We developed and implemented a person-centered navigation model integrated in primary care where patients were referred to the Access to Resources in the Community (ARC) study by their primary care provider (PCP). The purpose of this paper is to explore the lay navigators’ learning experience as reflected in their journals and present implications for education and health promotion practice. Sixty-six journal entries from two navigators were analysed. To code the data, we used a newly developed framework based upon the theory-informed lay navigator training programme. Five unique themes were identified: 1) Gaining and Building Trust, 2) Developing Empathy, 3) Experiencing Hope and Optimism, 4) Feeling Helplessness and 5) Celebrating Gains and Successes. The five themes identified paint a sequential picture of the journey of leading individuals from primary care to health and social community resources. This innovative approach has expanded our understanding of how navigators learn in practice, more specifically how they learn from patients and how they develop knowledge and skills in person-centered care. Incorporating reflective journaling as a regular practice provides situational awareness and leads to enhanced learning. Practising person-centered care also develops when a trusting and empathetic relationship is established.
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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 | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".