Lessons in Media Advocacy: A Look Back at Saskatchewan's Nursing Education Debate
Bibliographic record
Abstract
Nurses are encouraged to exert their influence in the realm of public policy, particularly policies related to the nursing profession, the health care system and the health of their clients. Media advocacy can be used by nursing organizations to mobilize public support on policy issues in order to influence policy makers. This article retrospectively examined the media advocacy efforts of nursing stakeholders in Saskatchewan, Canada in response to a new government policy that would have impacted educational requirements for licensure as a registered nurse (RN) in that province. Print media sources from the period January to March, 2000 were examined to determine the specific media advocacy techniques used by nursing organizations within the framework of the policy cycle. The success of nursing stakeholders in reversing the government position highlights the effectiveness of media advocacy as a tool to disseminate messages from the nursing profession in order to impact policy.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| 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".