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Record W4401368440 · doi:10.5737/23688076343381

Use and impact of virtual resources for peer support by young adults living with cancer: A systematic review

2024· review· en· W4401368440 on OpenAlexaffvenue
Riley Martens, Mary Hou, Susan Isherwood, Alison Hunter-Smith, May-Lynn Quan, Colleen Cuthbert

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2024
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsCINAHLPsychosocialSocial supportPsychological interventionPeer supportChecklistMEDLINECochrane LibrarySurvivorship curveSocial mediaMedicineIntervention (counseling)Inclusion (mineral)Young adultPsychologyCancerGerontologyMeta-analysisWorld Wide WebNursingPsychiatryPsychotherapistComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

Background: Young adults (YA) with cancer often have unmet psychosocial needs. The impact of peer support delivered in an online format to fulfil these needs in the populations of YA with cancer has not been thoroughly examined. Methods: We searched Cochrane Central Register of Controlled Trials, CINAHL, Embase, and Medline databases. We included articles about online peer support interventions for YA cancer survivors between the ages of 18 to 40 years old. Results: Our literature search yielded n = 2,773 articles and we obtained consensus on 12 articles for inclusion. We qualitatively synthesized these articles using data abstraction based on the Template for Intervention and Replication (TIDierR) checklist. Overall, six studies demonstrated correlation between online peer support and improved wellbeing of participants. Thus, online peer support may be useful for young cancer patients. Conclusion: This systematic review summarizes the current state of knowledge regarding the availability and evaluation of online YA peer support programs and reveals the need for further research in this field.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.000
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 head0.046
GPT teacher head0.398
Teacher spread0.352 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2024
Admission routes2
Has abstractyes

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