A narrative evaluation of a community‐based nurse navigation role in an urban at‐risk community
Bibliographic record
Abstract
AIMS: To explore community members' stories of their experiences with a Nurse Navigator programme serving an urban neighbourhood and primary care practice to address persistent health and social barriers adversely affecting health equity and well-being. BACKGROUND: In response to striking health and social inequalities existing across neighbourhoods in a large southern city in Ontario, Canada, a pilot programme was designed to improve health and social outcomes in a specific "at-risk" neighbourhood. The programme includes nurse-led navigation support for individuals and families and networking to facilitate improved service integration at a systems level. DESIGN: A narrative inquiry approach based on the Three-Dimensional Narrative Inquiry Space method, as described by Clandinin & Connelly (Narrative inquiry: Experience and story in qualitative research, ). METHODS: A thematic analysis of nine community members' life stories from narrative semi-structured interviews (January-June 2014) in conjunction with field notes, observations and documents. Participants' life stories created a common narrative of the experience of navigation in a community setting. FINDINGS: There were four main themes: "opening the door"; "more than just a conversation"; "making connections"; and "on a new trajectory". Participants valued the development of a therapeutic relationship, which optimized social inclusion, barrier reduction and connectivity to supportive health and social services. CONCLUSIONS: The relational process of navigation as an antecedent to barrier reduction has direct implications for programme development, continuing education of navigators and quality improvement of existing navigation services. Study findings have implications for development of navigation competencies for nurses working with priority populations to address health inequities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".