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Record W2751566868 · doi:10.3747/co.24.3512

User Survey of Nanny Angel Network, a Free Childcare Service for Mothers with Cancer

2017· article· en· W2751566868 on OpenAlexaffvenue
Lawrence B. Cohen, Naomi Schwartz, Amber Guth, Alex Kiss, Ellen Warner

Bibliographic record

VenueCurrent Oncology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsUniversity Health NetworkSunnybrook HospitalUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsDemographicsMedicineFamily medicineService (business)User satisfactionPatient satisfactionTelephone surveyNursingDemographyAdvertising

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of the present study was to determine user satisfaction with Nanny Angel Network (nan), a free childcare service for mothers undergoing cancer treatment. METHODS: All 243 living mothers who had used the nan service were invited by telephone to participate in an online research survey; 197 mothers (81%) consented to participate. The survey, sent by e-mail, consisted of 39 items divided into these categories: demographics, supports, use, satisfaction, and general comments. RESULTS: Of the 197 mothers who consented to receive the e-mailed survey, 104 (53%) completed it. More than 90% of the mothers were very satisfied with the help and support from their Nanny Angel. Many mothers mentioned that the Nanny Angel was most helpful during treatment and medical appointments, with 75% also mentioning that their Nanny Angel helped them to adhere to their scheduled medical appointments. However, 64% felt that they had not received enough visits from their Nanny Angel. CONCLUSIONS: Satisfaction with the nan childcare provider was high, but mothers wished the service had been available to them more often. Our study highlights the importance of providing childcare to mothers with inadequate support systems, so as to allow for greater adherence to treatment and medical appointments, and for more time to recover.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.186
GPT teacher head0.442
Teacher spread0.257 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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