The empirical-phenomenological research framework: Reflecting on its use
Bibliographic record
Abstract
Background and objective: Descriptive phenomenology when used within the tradition of Husserl offers the qualitative researcher a unique perspective into the lived experience of the phenomena in question. Methods of data analysis are often seen as the theoretical framework for which these studies are then focused. However, what is not realised is that the data analysis tool is merely that a tool for which to delineate the individual narratives. What is often missing is a research framework for which to structure the actual study. Therefore, the aim of this paper is to offer a reflective account of how the empirical-phenomenological framework shaped and informed a descriptive phenomenological study looking at the lived experience of male nursing students as they journey though the under-graduate nursing programme.Methods: A reflective narrative was used to examine and explore how the empirical-phenomenological framework can be used to support method construction within a descriptive phenomenological study.Results and conclusions: The empirical-phenomenological research framework aims to provide a practical method for understanding and valuing the range and depth of descriptive phenomenology, in particular the lived experience. Used in combination with specific phenomenological data analysis models the empirical-phenomenological framework is structured to support the qualitative research process.
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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 | 0.143 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.015 | 0.098 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".