Unspoken phenomena: using the photovoice method to enrich phenomenological inquiry
Bibliographic record
Abstract
Photovoice is a powerful method that is gaining momentum in nursing research. As a relatively new method in nursing science, the situatedness of photovoice within or alongside various research methodologies in a single study remains in a stage of early development. The purpose of this paper is to discuss the photovoice method as a means to elicit phenomenological data when researching the lived experience. While the foundational bases of phenomenology and photovoice differ substantially, the argument presented in this paper suggests that the photovoice method can be successfully used in phenomenological inquiry provided that significant rigour checks are pursued. This includes reflecting upon the origins and understandings of both methodology and method to promote methodological congruency. Data collection and analysis approaches that contribute to phenomenological inquiry using the photovoice method in addition to rigour and ethical considerations are discussed. The use of data generated from photovoice in phenomenological inquiry may fill a void of understanding furnished by limitations of traditional phenomenological inquiry and of spoken language and can enhance understanding of the lived experience, which may not always be best understood by words alone.
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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".