Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of Using Evidence in Health and Social Care is to encourage healthcare practitioners to ask 2 questions: what counts as evidence, and is this research true for me, in my setting?The book's self declared target audience is practitioners in health and social care "who want to read research and apply it in practice and not for people who want to do research themselves."This book, like any overview of a large subject, is likely to please some of the people some of the time and leave everyone just a little dissatisfied.Although it claims to be aimed at the broadly defined "practitioner," it seems better suited to students of the health oriented social sciences and is written from a social science perspective.The text is one used by the Open University School for Health and Social Welfare for the course on critical practice in health and social care.For medically oriented healthcare practitioners, this book offers insight into qualitative methods that are more common to the social sciences.It challenges those who are steeped in quantitative methods to think more broadly about "what evidence is" (or what can be known for sure).It will also challenge them to critically appraise the validity of this type of evidence.Unfortunately, not all of the chapters are of interest, nor are they uniformly well written.For example, the first chapter, "ways of knowing," is a treatise on "what can be known for sure" and is full of postmodernist jargon.My initial reaction, as a busy practitioner whom the authors claim to be targeting, was to throw the book across the room.However, as I continued through subsequent chapters, I learnt that the necessity of first considering what evidence is becomes a central theme in critically appraising both evidence and methods for specific issues in specific settings.The logic of beginning with this chapter became obvious, and I relented somewhat in my initial harsh appraisal.My concern remains, however, that the opening chapter may deter busy healthcare practitioners from persevering with the rest of the book, unless they are taking the course or writing a review.Why do I not dismiss this book immediately as having no interest to medically oriented healthcare practitioners?I suspect that practitioners with a social science background might find the initial chapters, such as "making sense of surveys" and "understanding experimental design," a comfortable and non-threatening overview of quantitative methods.In addition, a basic but useful list of types of survey bias is included along with an excellent discussion of why consumer satisfaction surveys are poor indicators of a system's performance.Furthermore, I found that the chapters "interpreting meaning" and "using action research" were an approachable and enlightening overview of the continuum of collaborative, interpretivist research methods.Excellent examples of this method were used as a way to elucidate values and meanings that may be unique to particular situations.These methods not only answer questions but also may be used to frame the relevant question and bring about change in processes or organisations.Also included are excellent practical discussions of using these methods rigorously to provide high quality evidence.Using Evidence in Health and Social Care challenges one to think of evidence and its validity in a wider context than that emerging from quantitative methods.I recommend specific chapters of this text for healthcare practitioners who want an accessible overview of qualitative, collaborative, participatory, and interpretivist research methods.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it