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Early childhood home visiting programme: factors contributing to success

2006· article· en· W1967283024 on OpenAlexaff
Maureen Heaman, Karen Chalmers, Roberta L. Woodgate, Judy Brown

Bibliographic record

VenueJournal of Advanced Nursing · 2006
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPsychological interventionNursingMedicineVisitor patternQualitative researchCurriculumPsychologyPedagogy

Abstract

fetched live from OpenAlex

AIM: This paper reports a study of the factors that public health nurses, home visitors and parents consider important for the success of an early childhood home visiting programme. BACKGROUND: The primary aim of early child home visiting programmes is to promote healthy and safe growth and development of infants and children in at-risk families. Few studies have focused on actual programme components which foster this outcome. METHODS: The research was a descriptive, qualitative evaluation. Success of the programme was defined as positive changes in families which were seen as directly related to participation. The 58 participants were 24 public health nurses, 14 home visitors and 20 parents. One in-depth semistructured audio-taped interview was conducted with each participant between October 2003 and February 2004. All interviews were transcribed and analysed using open coding; themes and categories were developed and reviewed for congruence of coding. FINDINGS: Participants discussed several factors that they considered important for the success of the programme: its particular characteristics, the programme activities and the healthcare providers. CONCLUSION: Components contributing to the success of early childhood home visiting programmes include a strength-based philosophy, voluntary enrollment of parents, regularly scheduled home visits, a curriculum to structure the home visitor's interventions, and careful attention to the selection, training, and supervision of home visitors.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.010
GPT teacher head0.302
Teacher spread0.292 · 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 designObservational
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
Published2006
Admission routes1
Has abstractyes

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