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Record W2567195727

Watching me, watching you : interpreting findings from a realist evaluation study

2014· article· en· W2567195727 on OpenAlexaboutno aff
Lynne Williams, Jo Rycroft‐Malone, Christopher R Burton

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsIntermediaryPsychological interventionContext (archaeology)DocumentationControl (management)Public relationsHealth careMillerMedical educationPsychologyMedicineNursingPolitical scienceBusinessManagementComputer scienceMarketing
DOInot available

Abstract

fetched live from OpenAlex

This poster is a report of part of findings from an original research study in the area of realist evaluation which was designed to evaluate the role of the ‘intermediary’ in promoting best practice in infection prevention and control. In healthcare, intermediary roles have potential to support the translation of evidence into everyday practice (Chew et al, 2013), especially through the ways in which they act to “bridge the communities of research and clinical practice” (Milner et al, 2005: 900). However, understanding the effectiveness of intermediary interventions is challenging, given the complexity of the processes involved and the context-dependent, contingent nature of their work (Chew et al, 2013: 337). Study aim and objectives: The aim of the study was to seek the programme theories to show how intermediaries promote best practice in infection prevention and control; to determine what works, for whom, how, and in what respects. Methods: Realist evaluation was used to elicit a better understanding of the mechanisms and contexts that lead to outcomes (CMOs) for the role of intermediaries in infection prevention and control programmes. An essential element which separates realist evaluations from other types of outcome-focused evaluations is the focus on understanding how programmes or services, which offer different resources, have different effects for people (Timmins & Miller, 2007). In this study, a realist review led to case studies conducted consecutively within two NHS hospitals in the United Kingdom, with data comprising of interviews, non-participant observations, and documentation review. Findings: In infection control practice, intermediaries discussed “watching over” as an inherent part of their role. To consider the implication for future policy and practice, this finding was explored through a Foucaldian lens, to understand how surveillance of people who operate in organisations and institutions (such as hospitals), is historically drawn from knowledge of the impact of discipline in social systems. The concept of informal surveillance represents the idea that equilibrium within society is maintained through covert forms of discipline (Burrell, 1988). Informal surveillance is generally undertaken through different interactions with colleagues and co-workers, whilst more formal surveillance is usually undertaken through formal institutions (Henderson et al, 2010). In this study, formal surveillance formed part of the intermediary role and included carrying out tasks, such as data collection and audit, to meet the objectives within the organisation’s infection control strategy. However, the informal surveillance intermediaries undertook was described as a subconscious activity, in an enactment of their intermediary role. Subjecting individuals to surveillance acts as a vehicle to enhance self -awareness and influence behaviour (Henderson et al, 2010), and there is an argument for surveillance to become more humanised (Lyon, 2003). Whilst surveillance is not always intentional, it is, according to Foucaldian thinking, an integral part of humanity. It is argued that much more needs to be understood of how different forms of surveillance can be used to promote best practice, and in particular, how can human surveillance be integrated into organisational systems which are already established. This poster will illustrate how promoting self-surveillance can make a significant contribution to promoting best practice in healthcare.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.054
GPT teacher head0.363
Teacher spread0.309 · 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; both teacher heads agree on what is shown here.

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
Published2014
Admission routes1
Has abstractyes

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