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Record W2895232564 · doi:10.11124/jbisrir-2017-003936

Context revisited: situations beyond our kin

2018· editorial· en· W2895232564 on OpenAlexaffabout
Christina Godfrey, Rosemary Wilson, Kim Sears, Amanda Ross‐White

Bibliographic record

VenueThe JBI Database of Systematic Reviews and Implementation Reports · 2018
Typeeditorial
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsExcellenceContext (archaeology)Active listeningHealth careQuality (philosophy)Public relationsPatient safetyMedicineMedical educationPsychologyDeveloping countryNursingPolitical scienceHistory

Abstract

fetched live from OpenAlex

During a recent training event of the Queen's Collaboration for Health Care Quality: a Joanna Briggs Institute Centre of Excellence (QcHcQ), at Queen's University in Kingston, Canada, attendees were discussing topics for a systematic review. These attendees were all library scientists, six of whom were from Sub Saharan African countries. One attendee posed an important question about strategies used to keep patients safe in a hospital setting. Even though our team at QcHcQ has extensive expertise in health care quality and patient safety in both developed and developing countries, our thoughts went to risks such as hospital acquired infections, falls and adverse drug events. However, one attendee went on to explain that patients were often assaulted in their hospital beds and was determined to investigate strategies to prevent these violations and keep patients safe. This alarming and unexpected extension of the bounds of the concept of patient safety highlights the importance of being aware of the context in which people live and work. Remaining mindful and open to listening to the experiences of health professionals is a key part of bringing evidence into practice globally and is consistent with the tailoring, problem solving and mutual learning activities that are part of the knowledge translation process.1-3 These activities address the “gap” between what is known and what is done that exists despite evidence synthesis and the development of important clinical guidelines that are accessible in low- and middle-income countries (LMICs).4 Examination of the context of care in diverse settings, such as those in LMICs, must go beyond cursory description and surface assessment of current practice and processes. Collaborators who seek to work together to achieve contextualized evidence for care in LMICs must focus on engagement with local colleagues: to dig deep in environments to uncover the features of society and culture, structure, relationships and resources encountered in these settings. For example, in a recent study of clinician and environmental factors affecting pain care in one LMIC, Nyirigira et al.5 found that factors in organizational culture and clinical resource allocation were cited by clinicians as barriers to the sustainable implementation of evidence-informed care. Bayou6 also described the difficulties clinicians face in breaking free of established practices to innovate and “lead change” in evidence informed practice, particularly when the evidence is not produced locally. Okwen confirmed this challenge and added a call to develop innovations to challenge the status quo while recognizing the financial barriers to evidence use in LMICs.7 There is a fundamental need to bring together clinicians, policy-makers and researchers in LMICs to define concepts like quality of care at a cultural level to avoid inappropriate comparisons that can add increase barriers to evidence use in LMIC. Jayasekara and Shultz provide an example from curriculum development with their recommendation for assessment of cultural relevance of concepts that are appropriate to developed countries prior to implementation.8 Meaningful and mutual engagement in the contextualization of evidence for practice takes time. Collaborators from developed countries need to be prepared to provide consistent support and to give as well as receive guidance from local colleagues in the knowledge translation process. Ultimately, the sustainability of evidence informed implementation projects depends on its appropriateness and feasibility in the environment and the commitment of the setting to a local definition of quality.

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)0.051
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.098
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0040.004
Science and technology studies0.0550.076
Scholarly communication0.0260.053
Open science0.0050.050
Research integrity0.0190.045
Insufficient payload (model declined to judge)0.0150.002

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 head0.170
GPT teacher head0.544
Teacher spread0.374 · 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 designNot applicable
Domainnot available
GenreEditorial

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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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