Bibliographic record
Abstract
Is QA investment worth it?The evidence is familiarscreening the population and preventing ill health and healthcare complications, especially in the young can save billionsbut at what personal and economic cost to service providers?Doug Ford and Dick Zoutman in this issue explore quality improvement (QI) success and failure from an employee perspective.Their on-line survey measured QI project time, effort, commitment, reward, benefits and downsides in Canadian acute hospitals.Survey response rates, despite follow-up e-mails, were disappointing, which rings warning bells about QI project commitment and engagement.On the upside, however, data were gathered from 125 acute hospitals, which improves external validity (the extent to which findings can be generalised).Encouragingly, respondents, despite their negativity, felt that QI projects improved patient safety and service quality.Also encouraging were the hospital QI projects' breadth and depthnotably how intractable healthcare problems (such as hospital acquired infections) were being solved.The major downsides included competition between QI project demands and healthcare professionals' clinical duties.That is, worryingly, it is the key stakeholders (nurses and doctors) most affected by these competing demands and, therefore unsurprisingly, are the less committed professionals.Surprisingly, QI education and training programmes were not universally supported.Manager and leader commitment, on the other hand, featured in bucket loads.Clearly, the challenge is to study how best to support and encourage clinicians to become more engaged with QI.Chinweike Eseonu and colleagues also underline successful QI project structures and processes.They explore QI project drivers and barriers in North America.Although using an unusual theoretical framework and a more triangulated (quantitative and qualitative) approach than Ford and Zoutman, Eseonu et al., unearth similar outcomes.Their respondents also believed that QI projects improved service delivery, but felt aggrieved that insufficient time and resources were provided for CI work and how professionals were expected to deliver QI and clinical goals simultaneouslyalso the Ford and Zoutman study respondents' biggest gripe.The Eseonu et al., study participants' main grievance, however, unlike Ford and Zoutman, was managerial commitment to and support for QI projects.Both studies share common ground, but it is clear that ventures in different contexts need bespoke approaches if they are to be successful and sustainable.One scenario possibly worse than maintaining unpopular QI projects is not knowing what quality is like because service provision is unmeasured.Health and social care permutations mean that there always will be services where quality has not been measured or where quality assurance data are too old; so the adage that we should not change anything that has not been measured applies.In this issue, Vigdis Grøndahl and Liv Fagerli measure Norwegian nursing home service quality is some detaila challenging research and development (R&D) topic owing to residents' questionable mental capacity.Their cluster analysis reveals a significant elderly group who are dissatisfied with many services.Clearly, the inter-relationships between service domains in the negative elderly resident cluster are complex, which presents managers with a challenge.The growing elderly population, most having made significant contributions to their country, with increasing co-morbidities, mean that they are a sector deserving the best care and service monitoring that can be mustered.It does not matter whether the organisation in which we work is large or small, one irritation is not being able to find a file or documenta problem in hospitals, which is dangerous.Records control, therefore, is paramount and it is little wonder that records departments are being accredited and certificated using ISO standards.Owing to the myriad
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.224 | 0.109 |
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".