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Record W3203929840 · doi:10.1177/10748407211048217

The Development of an Early Intervention for Supporting Families of Persons With Acquired Brain Injuries: The SAFIR <sup>©</sup> Intervention

2021· article· en· W3203929840 on OpenAlexaboutno aff
Véronique de Goumoëns, Koffi Ayigah, Philippe Ryvlin, Anne‐Sylvie Ramelet

Bibliographic record

VenueJournal of Family Nursing · 2021
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsnot available
FundersHaute école Spécialisée de Suisse OccidentaleUniversité de Lausanne
KeywordsIntervention (counseling)Perspective (graphical)MedicineStakeholderPsychologyNursingComputer science

Abstract

fetched live from OpenAlex

Families of persons with acquired brain injuries need to be supported from the early phase of hospitalization. To date, no known early family intervention is available for this population. Using the Medical Research Council Framework, we developed a new intervention based on the Calgary Assessment and Intervention Models that includes the family preferences, clinician’s expertise, and the contextual resources. This paper aims to describe the complete development process including a scoping review, an assessment of families and clinicians’ needs, an evaluation of the contextual resources, and an adaptation of the theoretical framework. Using a systemic perspective, we tailored the new intervention to involve the stakeholder’s preferences. The result is an early family intervention named SAFIR © , led by a clinical nurse specialist, including five core components and structured around three phases and a follow-up. The next steps will be focused on assessment of the clinical feasibility of this new intervention.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.414
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.075
GPT teacher head0.408
Teacher spread0.333 · 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.

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

Citations5
Published2021
Admission routes1
Has abstractyes

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