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Record W2983259971

Practices and Perspectives of Music Therapists Working With Infants in Canadian Neonatal Intensive Care Units

2019· dissertation· en· W2983259971 on OpenAlexaboutno aff
Laura A. Hastings

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2019
Typedissertation
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
FundersStrong
KeywordsMusic therapyPsychological interventionContext (archaeology)Qualitative researchWorkloadIntensive careNursingPsychologyMedicineNeonatal intensive care unitCertificationContent analysisMedical educationPsychotherapistPsychiatryPolitical scienceSociologyManagement
DOInot available

Abstract

fetched live from OpenAlex

This qualitative descriptive research investigated the current practices and perspectives of certified music therapists (MTA) working in neonatal intensive care units (NICUs) in Canadian hospitals. The Canadian context is important to consider because of this country’s unique healthcare landscape, and because the use of NICU music therapy in Canada is relatively new. Three individual interviews were recorded, transcribed, and analyzed according to qualitative content analysis procedures. Results include nine overarching categories containing multiple themes. These categories include: weekly workload, referrals, assessment, evaluation, music therapy interventions, challenges of the job, rewards of the job, evolution of NICU music therapy practices, and recommendations for advancing Canadian music therapy practices. Implications for the music therapy profession, practice and continuing education, as well as implications for Canadian hospitals and recommendations for research are presented. Limitations of the study are identified. It is the researcher’s hope that this study will help to promote the development of Canadian NICU music therapy programs, thus increasing Canadians’ access to this type of innovative service.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.288
Teacher spread0.245 · 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.

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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

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