Incorporating health literacy into family nurse practitioner practice: an integrative literature review
Bibliographic record
Abstract
In Canada, primary health care (PHC) has been endorsed by numerous reports and health care researchers as a mechanism for improving the health and well-being of all Canadians. The family nurse practitioner (FNP) plays an important role in the provision of PHC in Canada. As relatively new providers of primary health care, FNPs have struggled to demonstrate how their patient-centred focus can contribute to positive patient outcomes. The Canadian Nurses Association describes competencies that ensure that FNPs employ a patient-centred focus, but missing is a conceptualization of a framework that offers up a way for FNPs to describe and measure those competencies that allow them to practice in a way that is patient-centred. The concept of health literacy offers a means to address this gap. An integrative literature review was conducted to examine how FNPs can incorporate health literacy into their practice in order to demonstrate their patient-centred competencies and thereby make an impact on patient outcomes. The review of the literature revealed that working from a framework of health literacy promotion offers opportunities to describe and better provide the value-added, public health component of FNP practice, how this component relates to patient-centredness, and how by implying the framework, FNPs can impact patient outcomes. While there is not yet consensus within the literature as to what health literacy frameworks and strategies are most applicable to practice, this in itself presents important opportunities for FNPs to contribute to an emerging body of knowledge. --Leaf ii.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".