Bibliographic record
Abstract
The preparation of healthcare practitioners is the foundation platform upon which a career in healthcare is built. This preparation is strictly regulated with content and competences clearly articulated. That said the challenge to ensure that pain is a competency of every nurse has not yet been endorsed in the UK. Here in Canada the Canadian Nurses Association stipulates that the competencies required of a registered nurse will include pain assessment, prevention and management. The alignment of competencies with the needs of the population is essential. The preparation of nurses should be closely aligned with the health needs of the population and attempts have been made to move the focus from hospital to community settings; with a resultant shift from an emphasis on disease to health and wellness. There is a greater focus on the management of long-term conditions and recognition that much of the care will take place in community settings. It would seem logical that the content and competences required for registration reflects the growing burden of chronic illness as evidenced by high prevalence rates in the UK for hypertension, cardiovascular disease, diabetes and obesity to name a few (Craig and Mindell, 2008). The survival rates for cancer are increasing which is good news but many people experience chronic pain due to their treatment. It would appear that we are not good at managing pain. It has been reported to be suboptimal in hospitals and the community for over 40 years and yet the alignment between educational preparation of health professionals to manage pain and the burden of chronic pain in the population appear to be grossly mismatched. The reasons for this are many but the one which is most agreed upon is that the pre-registration pain education, across the professions, is woefully inadequate. A survey conducted in Canada across universities and health professions, including veterinary medicine, found that veterinary students received five times the pain education of doctors (Watt-Watson et al. 2009). This prompted media attention and the appearance of a large dog on the front page of Maclean’s magazine (Canadian current affairs) with the caption ‘your dog gets better healthcare than you’. The same study was adapted and replicated in the UK and revealed similar findings with
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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 | 0.036 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.014 | 0.034 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.014 | 0.032 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".