Investigación cualitativa en Cuidados Paliativos. Un recorrido por los enfoques más habituales
Bibliographic record
Abstract
La utilización de la investigación cualitativa en Cuidados Paliativos (CP) está en auge, quizás porque tienen muchos aspectos en común. Ambos se centran en la persona y su entorno y están especialmente interesados en la experiencia humana. El objetivo de este artículo es presentar algunos de los enfoques más frecuentemente utilizados en las ciencias de la salud, proporcionando ejemplos de estudios de CP. Esto con el fin de ayudar a quienes se están iniciando en la investigación cualitativa a explorar los posibles enfoques que podrían utilizar para realizar investigación en CP. A través del ejercicio «armchair walkthrough», se concretan los aspectos clave de un proyecto de investigación, considerando los distintos enfoques: la etnografía, la fenomenología, la narrativa y la teoría fundamentada. Familiarizarse con la metodología cualitativa y algunos de los enfoques ayudará a los profesionales de CP a plantear nuevas preguntas y retos con investigación rigurosa. The use of qualitative research in Palliative Care (PC) is increasing, probably because PC and qualitative methodology have many things in common. Both focus on the person and his or her environment, and they are particularly interested in human experience. The aim of this paper is to present some of the most often used qualitative research approaches in health science, providing examples of PC studies. The aim is to help beginners to explore the possible approaches that they could use to carry out research in PC. The armchair walk-through exercise, which helps to specify key aspects in research, is developed for each of the approaches: ethnography, phenomenology, personal narrative, and grounded theory. Becoming familiar with qualitative methodology and some of the approaches will help PC health professionals to raise new questions and address new challenges with rigorous research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".