Understanding the Meaning Attributed to Patients’ Lived Experience Beyond a Diagnostic Label: Applications of Interpretive Phenomenology to Nursing Practice
Bibliographic record
Abstract
Unearthing the meaning patients' attribute to their lived experience beyond a diagnostic label has profound implications to reconceptualize the caring art of nursing practice. Featured throughout this article are lessons learnt from caring for a patient who had received a stigmatizing diagnosis of liver cirrhosis. Contained within such heavily stigmatized diagnoses are various preunderstandings (e.g., negative views about substance misuse) that are often informed by dominant societal beliefs, which can impede nurses' ability to authentically care for patients across the lifespan. Interpretive phenomenology is a philosophical tradition dedicated to the description of lived experience; this philosophy can facilitate an understanding of how things appear in the world in which they exist. By applying interpretive phenomenology to nursing practice, nurses can understand the essence that patients' attribute to their diagnostic label by authentically exploring their lifeworld. Utilizing the philosophical underpinnings of interpretive phenomenology, this article proposes opportunities to inform the way nurses care for patients by exploring how a Gadamerian fusion of horizons and holistic approach to care can reconceptualize nursing practice. Understanding the meaning patients' attribute to their lived experiences is a critical step to optimizing care by granting nurses the opportunity to ascribe meaning in their everyday clinical practice, which can motivate positive patient outcomes and a continuous evolution of their authentic being.
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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 | 0.042 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.062 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".