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Record W2355906629

Research partnership and knowledge transfer in the development of a generic evaluation toolkit for health promotion interventions in primary care.

2010· article· en· W2355906629 on OpenAlexaboutno aff
Aideen M. Dunne, Angela Scriven, Andrew Howe

Bibliographic record

VenueInternational public health journal · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsHealth promotionPublic relationsHealth policyHealth careMedicineGeneral partnershipPublic healthNursingPolitical science
DOInot available

Abstract

fetched live from OpenAlex

IntroductionEvaluation research is fundamental to generating evidence to inform the development of policy, services and practice in health promotion. At a national and international level, rigorous evaluation is central to advancing knowledge and practice. At a local level evaluation can measure the effectiveness and efficiency of health promotion interventions. This evidence can be used to inform decision making about budget allocations and recurrent funding, service provision and methods for meeting local health needs.The speed at which health promotion practice and knowledge is advancing is potentially stalled by a number of complexities associated with the process of evaluating health promotion. These complexities are well reported and are related to the individual nature of health (1,2), the wide range of activities and actions considered under the term health promotion (3-6), the lack of a universal definition of evaluation, and a lack of clear guidelines as to what constitutes good evaluation (3,7).Health promotion is a complex concept; this is partly due to the fact that health promotion draws from many different disciplines and ideologies and as a result there is no universally accepted definition or concept of the field of practice (8).Health promotion, in its broadest terms, aims to have a health enhancing effect (3). The Ottawa Charter for Health Promotion indicates that the activities of health promotion are extremely broad ranging from operating at an individual level (through developing personal skills) to targeting health indirectly at a population level through policy changes, making adjustments to the physical environment or re-orientating health service provision (5). Not only does health promotion encompass many different activities and draw from many disciplines, but to be effective, a combination of health promotion activities is recommended (8,9).Therefore, the challenge of evaluation is not only to capture the effects of a health promotion activity but also to capture the likely interaction between activities, and their combined effect on health, either directly or indirectly. Without clear parameters or rules about what constitutes good evaluation practice it then becomes difficult to know what to evaluate and what we understand as evidence of effectiveness (3,7,10). This adds another layer of complexity to the process, and another degree of ambiguity.Evaluation, in its simplest form is the comparison of an object of interest against a standard of acceptability (11). Evaluation of health promotion, in its most concise form, has three foci; the process of implementing the activity being evaluated, and the short and long term achievement of an intervention objectives (12). As discussed, a health promoting intervention's objectives may be far reaching, and directly concerned with achieving a health gain, or they may be concerned with indirectly targeting health through health behaviours, service provision, addressing structural disadvantage or changing a policy.Currently, there are inconsistencies in how the evaluation of health promoting interventions is conducted, and how the findings are interpreted and reported (10, 13). This lack of standardisation of evaluation has been identified at an international level and recent international research collaborations have contributed to progress in this area (3, 14). This has led to some progress towards standardisation, with Glasgow and colleagues (15,16), Steckler and Linnan (13) and Baranowski and Stables (17) making recommendations for the core components of process evaluation, and Nutbeam (18), Bauer and colleagues (19) and Spencer and colleagues (20) presenting models for the classification of health outcomes (20). However, as discussed earlier, health promotion is a complex process, and to capture this, there is a real need for an integrated evaluation framework which builds on existing knowledge and identifies the relationship and connections between process, impact and outcome evaluation data. …

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)0.096
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0960.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.768
GPT teacher head0.658
Teacher spread0.109 · 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 teacher head, not a consensus.

Study designOther design
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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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