Bibliographic record
Abstract
Cancer survivors report a gap in work integration advice from healthcare professionals. This occurs despite physicians routinely providing comment upon survivors’ work abilities to insurers and employers. In order to understand the phenomena of survivors’ work integration from physicians’ perspectives, a vignette methodology was used. Vignettes were chosen as a means to explore physicians’ perspectives in a non-confrontational and sensitive manner. Vignettes, composed of photographs and narratives reflective of survivors’ lived experiences of work integration were presented to 10 physicians during individual interviews. In this manuscript, I outline my experience using vignettes, the learning I have achieved, and the modifications I intend to make before again similarly using vignettes with another stakeholder group. In this study, receptive participants expressed enjoyment of the real-life nature of the vignettes through which significant insights into the phenomena were successfully gleaned. In this case, vignettes were an effective means to sensitively explore physicians’ perspectives of cancer survivors’ work integration. Yet, considerations that I will undertake prior to the next stakeholder inquiry include incorporating findings from the physicians and modifying the presentation of survivors’ findings to be more applicable to that next stakeholder, reconsidering the number of vignettes used as well as the terminology and delivery mechanism, and refinement of questioning format.
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 | 0.021 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".