Bibliographic record
Abstract
This concluding article comments on what we learned from the conference, what we still need to know, and what we need to do now. It describes what participants said about the impact of the conference and the follow-up steps that have been taken so far. In terms of what we learned, there was agreement on the importance of culture in understanding literacy and health literacy; the importance of context; the integral relationship between literacy and health literacy and the concept of "empowerment;" the value of efforts to improve health through literacy and health literacy; and the need for collaboration. We need more and better information on how our various efforts are working; the cost of low literacy; the links between health, education, and lifelong learning; the needs and strengths of Aboriginal people, and the perspectives of Francophone and ethnocultural groups. Specific topics worthy of pursuit are suggested. They are followed by a list of recommendations from the conference related to focussing on language and culture, and to building best practices, knowledge, and healthy public policy. The paper presents some findings from the conference evaluation, which suggests that the conference met its goals. It concludes by reporting on actions that have been taken to implement the conference recommendations, including the establishment of a Health Literacy Expert Committee and the submission of several funding proposals.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.118 | 0.051 |
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".