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Record W2517787876 · doi:10.1186/s12913-016-1613-1

Effect of care management program structure on implementation: a normalization process theory analysis

2016· article· en· W2517787876 on OpenAlexaff
Jodi Summers Holtrop, Georges Potworowski, Laurie Fitzpatrick, Amy Kowalk, Lee A. Green

Bibliographic record

VenueBMC Health Services Research · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of Alberta
FundersAgency for Healthcare Research and Quality
KeywordsNursing researchHealth administrationHealth informaticsMedicineNormalization (sociology)Public healthNursing managementProcess (computing)Process managementNursingComputer scienceProgramming language

Abstract

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BACKGROUND: Care management in primary care can be effective in helping patients with chronic disease improve their health status, however, primary care practices are often challenged with implementation. Further, there are different ways to structure care management that may make implementation more or less successful. Normalization process theory (NPT) provides a means of understanding how a new complex intervention can become routine (normalized) in practice. In this study, we used NPT to understand how care management structure affected how well care management became routine in practice. METHODS: Data collection involved semi-structured interviews and observations conducted at 25 practices in five physician organizations in Michigan, USA. Practices were selected to reflect variation in physician organizations, type of care management program, and degree of normalization. Data were transcribed, qualitatively coded and analyzed, initially using an editing approach and then a template approach with NPT as a guiding framework. RESULTS: Seventy interviews and 25 observations were completed. Two key structures for care management organization emerged: practice-based care management where the care managers were embedded in the practice as part of the practice team; and centralized care management where the care managers worked independently of the practice work flow and was located outside the practice. There were differences in normalization of care management across practices. Practice-based care management was generally better normalized as compared to centralized care management. Differences in normalization were well explained by the NPT, and in particular the collective action construct. When care managers had multiple and flexible opportunities for communication (interactional workability), had the requisite knowledge, skills, and personal characteristics (skill set workability), and the organizational support and resources (contextual integration), a trusting professional relationship (relational integration) developed between practice providers and staff and the care manager. When any of these elements were missing, care management implementation appeared to be affected negatively. CONCLUSIONS: Although care management can introduce many new changes into delivery of clinical practice, implementing it successfully as a new complex intervention is possible. NPT can be helpful in explaining differences in implementing a new care management program with a view to addressing them during implementation planning.

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)0.104
metaresearch head score (Gemma)0.203
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.549

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1040.203
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0060.005
Science and technology studies0.0040.009
Scholarly communication0.0070.007
Open science0.0040.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.190
GPT teacher head0.711
Teacher spread0.521 · 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 designQualitative
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

Citations40
Published2016
Admission routes1
Has abstractyes

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