Bibliographic record
Abstract
The term primary health care is now entrenched in our minds and our actions. A health for all by the year 2000 strategy is being examined to determine what has been achieved and what has not. All countries and most professions, including nursing, are scrutinizing the progress they have made towards achieving PHC. It is my intention, in this discourse, to move from an historical view to present-day concerns as they relate to the achievement of PHC. It will be impossible to do more than mention many of the latter, but I hope to set the stage for the articles that follow in this issue of the Journal. Historical Perspective Nursing has been involved in the development of PHC from the beginning. Concerns about the state of basic health care surfaced in 1973 (World Health Organization [WHO], 1973), when alarming states of health and vast gaps in health services for populations in developing countries were identified. An Expert Committee on Community Health Nursing was convened to recommend ways in which nursing might make a real impact on urgent problems throughout the world (WHO, 1974). The Committee made recommendations on: (1) the development of community health nursing services responsive to community needs in order to ensure PHC coverage for all, (2) the reformulation of basic and post-basic nursing education to prepare nurses for community health nursing, and (3) the inclusion of nursing in rational distribution and appropriate utilization in support of nursing personnel.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.046 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 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".