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Record W3185559753 · doi:10.1111/jpm.12788

Adoption of care management activities by primary care nurses for people with common mental disorders and physical conditions: A multiple case study

2021· article· en· W3185559753 on OpenAlexafffund
Ariane Girard, Édith Ellefsen, Pasquale Roberge, Joëlle Bernard‐Hamel, Catherine Hudon

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2021
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersRéseau de recherche portant sur les interventions en sciences infirmières du Québec
KeywordsCollaborative CareNursingContext (archaeology)Primary nursingMedicineQuality (philosophy)Health careUnlicensed assistive personnelPsychologyPrimary careNurse educationPublic healthHealth policyFamily medicineHRHISPolitical science

Abstract

fetched live from OpenAlex

WHAT IS KNOWN ON THE SUBJECT?: The collaborative care model is a well-known model to improve care quality for people with common mental disorders and physical conditions in primary care. The role of care manager is central to the collaborative care model, and primary care nurses are well-positioned to play that role. Adopting the role of care manager by primary care nurses is challenging due to several contextual factors; however, few implementation studies examined the context and current practices before implementing the role of care manager and the collaborative care model. WHAT THE PAPER ADDS TO EXISTING KNOWLEDGE?: The paper contributes to the advancement of knowledge about the pre-assessment of current practices before implementing the collaborative care model and the role of care manager. The paper offers a better understanding of the relationships between the context and the performance of care management activities by primary care nurses. The paper describes an innovative analysis technique to assess the gap between care management activities recommended in the collaborative care model and actual nursing activities. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Primary care nurses would benefit from having timely access to clinical support from mental health nurse practitioners in order to build their competency. Determinants of practice and the analysis technique to assess current practices will help other researchers or quality improvement teams to develop their plan when implementing the role of care manager. ABSTRACT: Introduction Few studies assessed current nursing practices before implementing the collaborative care model and the role of care manager for people with common mental disorders (CMDs) and physical conditions in primary care settings. Aim Evaluate the main determinants of practice that influence the adoption of care management activities by primary care nurses for people with CMDs and physical conditions. Methods A qualitative multiple case study was conducted in three primary care clinics. A total of 33 participants were recruited. Various data sources were combined: interviews (n = 32), nurse-patient encounters' observations (n = 7), documents and summaries of meetings with stakeholders (n = 8). Results Seven determinants were identified (1) access to external mental health resources; (2) clarification of local CMD care trajectory; (3) compatibility between the coordination of nursing work and the role of care manager; (4) availability of mental health resources within the primary care clinic; (5) competency in care management and competency building; (6) responsibility sharing between the general practitioner and the primary care nurse; and (7) common understanding of the patient treatment plan. Implications for practice To build their competency in care management for people with CMDs, primary care nurses would benefit from having clinical support from mental health nurse practitioners.

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)0.008
metaresearch head score (Gemma)0.018
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 score0.021
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.002
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.344
Teacher spread0.334 · 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".

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Citations6
Published2021
Admission routes2
Has abstractyes

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