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Record W4367030253 · doi:10.3138/cjpe.25.001

Gauging Alignments: An Ethnographically Informed Method for Process Evaluation in a Community-Based Intervention

2010· article· en· W4367030253 on OpenAlexaffvenueabout
Bonnie K. Lee, Donna Lockett, Nancy Edwards

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of OttawaUniversity of Lethbridge
Fundersnot available
KeywordsOperationalizationPsychological interventionProcess (computing)Intervention (counseling)Field (mathematics)EthnographyComputer sciencePsychologyKnowledge managementSociologyEpistemology

Abstract

fetched live from OpenAlex

Abstract: Community-based projects feature multidimensional interventions and interactions within unpredictable contexts. Process evaluations can shed light on variability in outcomes across sites and the reasons why some project outcomes fall short of expectations. The authors present an ethnographically informed study of the interactive project components in a pilot community-based falls prevention project that was implemented in 4 communities across Canada. Ethnographic descriptions and analyses of alignments between multilevelled project components allowed the researchers to better understand the mechanisms of project evolution at each site and variations in project momentum, mobilization, and sustainability across sites. Primary data sources consisted of project teleconference transcripts triangulated with log notes, field notes, and interviews. Descriptions and analyses of alignments may be instrumental to process evaluation. Project adjustments could then be made accordingly in propelling progress toward program objectives, informing program decisions, and in making sense of variability in program outcomes. Further exploration and operationalization of the alignment concept is recommended to advance knowledge about how to conduct process evaluations of complex interventions.

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)0.067
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0670.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.721
GPT teacher head0.743
Teacher spread0.022 · 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 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".

Quick stats

Citations3
Published2010
Admission routes3
Has abstractyes

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