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Record W4390956922 · doi:10.5334/ijic.icic23269

Towards More Appropriate Care for Low Back Pain: Understanding Primary Care Factors that Increase Best Practices and Collaborative Approaches to Care

2023· article· en· W4390956922 on OpenAlexaffabout
Nav Baldeo

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImplementation researchBest practiceContext (archaeology)Psychological interventionHealth careMedicineIntervention (counseling)NursingMedical educationPsychologyPolitical science

Abstract

fetched live from OpenAlex

Moving towards more appropriate care for low back pain is an important health services research issue in Ontario, Canada especially as evidence suggests there is overuse of health care resources for this problem. Low back pain involves excessive imaging, over-referrals for specialist care, and overuse of drug therapy. At the present time, there is also poor uptake of best practices, an evidence to practice gap, and low back pain management and outcomes have changed little over the past 35 years. The emerging field of implementation science will be useful to address the central research question which asks “What factors amongst primary care physicians that manage low back pain, enable or constrain their ability to implement best practices in their care?“ Implementation science (IS) is drawn upon to help advance the uptake of research findings into practice and provide understanding as to why the implementation of evidence into practice may be successful or not. Using the Consolidated Framework for Implementation Research (CFIR) model helps to identify barriers and facilitators to the uptake of best practices. Older frameworks have focused narrowly on physician behavior change, but CFIR recognizes that implementation must also consider features of context and setting to have a greater chance of success. As such, CFIR informs the study by using a meta-analytic framework, and focuses on elements of intervention characteristics, outer setting, inner setting, individual characteristics and process, which have been found to influence uptake of interventions. The study uses a mixed methods approach which is appropriate when attempting to understand the social and practice environment involved in providing primary care. In this multi-phase study, a literature review was first conducted to provide a current understanding of what is currently known on the topic. This informed Phase 2, which involved sending quantitative surveys to a sample of Ontario primary care providers, mainly including family physicians. A final Phase 3 involved qualitative in-depth interviews with a subset of those providers to provide context and help explain patterns in the research findings. Results from 240 surveys will be presented, including a discussion of descriptive statistics, inferential statistics, hypothesis testing and a principal components analysis (PCA). Findings from 21 interviews will also be discussed, where 7 themes and multiple subthemes were found, with a particular emphasis on: the usage of evidence, inter-professional practice, changing practice patterns, expanding virtual care, and centering patient preferences. Overall, multiple barriers and facilitators for the adoption of best practices including the use of clinical practice guidelines will be discussed. The research committee, engaged in the project co-design, is made up of an interdisciplinary team of health service researchers, clinicians and academics with diverse expertise and experience. The study aims to address gaps in the application of best practices for providers delivering low back pain care and recommends more integrated models of care. An overall goal is to help improve low back pain management at the primary care level, as this remains a prevalent and costly issue for the Canadian province of Ontario.

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

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)0.074
metaresearch head score (Gemma)0.190
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.267
Threshold uncertainty score0.530

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0740.190
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.006
Science and technology studies0.0070.008
Scholarly communication0.0170.014
Open science0.0040.010
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.651
GPT teacher head0.566
Teacher spread0.085 · 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

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes2
Has abstractyes

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