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Record W1976868198 · doi:10.1136/ebm.6.3.71

Using Evidence in Health and Social Care

2001· article· en· W1976868198 on OpenAlexaff
Howard Abrams

Bibliographic record

VenueEvidence-Based Medicine · 2001
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity Health NetworkUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsSocial careHealth careSubject (documents)PsychologyPublic relationsNursingMedicinePolitical scienceComputer scienceLibrary science

Abstract

fetched live from OpenAlex

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.

How this classification was reachedexpand

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 armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.213
metaresearch head score (Gemma)0.268
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score0.970

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2130.268
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0120.010
Science and technology studies0.0070.063
Scholarly communication0.0460.043
Open science0.0060.024
Research integrity0.0160.027
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.420
GPT teacher head0.547
Teacher spread0.127 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical · Commentary

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".

Quick stats

Citations68
Published2001
Admission routes1
Has abstractyes

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