Capturing Lived Experience: Methodological Considerations for Interpretive Phenomenological Inquiry
Bibliographic record
Abstract
Interpretive phenomenology presents a unique methodology for inquiring into lived experience, yet few scholarly articles provide methodological guidelines for researchers, and many studies lack coherence with the methodology’s philosophical foundations. This article contributes to filling these gaps in qualitative research by examining the following question: What are the key methodological and philosophical considerations of leading an interpretive phenomenological study? An exploration of interpretive phenomenology’s foundations, including Heideggerian philosophy and Benner’s applications in health care, will show how the philosophical tradition can guide research methodology. The interpretive phenomenological concepts of Dasein, lived experience, existentialia, authenticity are at the core of the discussion while relevant methodological concerns include research paradigm, researcher’s stance, objective and research question, sampling and recruitment, data collection, and data analysis. A study of pediatric intensive care unit nurses’ lived experience of a major hospital transformation project will illustrate these research considerations. This methodological article is innovative in that it explicitly describes the ties between the operational elements of an interpretive phenomenological study and the philosophical tradition. This endeavor is particularly warranted, as the essence of phenomenology is to bring to light what is taken for granted, and yet phenomenological research paradoxically makes frequent assumptions concerning the philosophical underpinnings.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.553 | 0.509 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.017 | 0.079 |
| Scholarly communication | 0.030 | 0.035 |
| Open science | 0.011 | 0.024 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".