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Record W2127058716 · doi:10.1177/1074840708330447

Undergraduate Nursing Students Writing Therapeutic Letters to Families

2009· article· en· W2127058716 on OpenAlexaboutno aff
Christen Erlingsson

Bibliographic record

VenueJournal of Family Nursing · 2009
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationNursingPsychologyMedicine

Abstract

fetched live from OpenAlex

Writing therapeutic letters to families is discussed in this article as an educational strategy encouraging students to think reflectively about family nursing. At the University of Kalmar, Sweden, undergraduate nursing students in a primary care module interviewed families using the Calgary Family Assessment Model and wrote therapeutic letters to these families. This article describes (a) the examination process, which was the context for writing therapeutic letters, (b) results of analyses of the letters, and (c) student's post-examination evaluation comments. Results indicate that most students needed encouragement to focus on the family's strengths and resources instead of focusing on own feelings or problems they perceived the family as having. Students also needed support in relinquishing their hierarchical role of "expert nurse." Students' evaluation comments showed that writing therapeutic letters provided students with opportunities to reflect about the connections between family nursing theory and the family itself.

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0180.007

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 head0.031
GPT teacher head0.371
Teacher spread0.341 · 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

Citations17
Published2009
Admission routes1
Has abstractyes

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