Bibliographic record
Abstract
This research examines if the process of creating and using a participatory photonovel can empower immigrant ESL-speaking women and also act as a tool to educate these women about a specific health topic. Data were collected through a) two separate interviews with each participant, b) two focus groups, c) field notes during the meetings the author had with the women once a week, and d) photographs of the photonovel project. The women created a participatory photonovel about nutrition entitled From Junk Food to Healthy Eating: Tanya’s Journey to a Better Life (to view this photonovel go to: e]http://www.photonovel.ca). The findings demonstrate that the photonovel can be an effective health literacy tool for immigrant ESL-speaking women, that it created community among the women, that it helped the women feel important and that it shifted their consciousness about nutrition in Canada. More funding should be given towards participatory research to ensure that ways to address the health literacy needs of ESL-speaking immigrant women in Canada match their needs. This means researching ways to create health literacy materials that have visuals that are representative of the diverse population of Canadians and with language that can be understood. In order to ensure that health literacy materials are going to be effective, it is essential that the participants be involved in the process.
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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.042 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 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".