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Record W2337930704 · doi:10.5737/23688076262104111

A mixed method study of a peer support intervention for newly diagnosed primary brain tumour patients

2016· article· en· W2337930704 on OpenAlexaffvenue
Douglas Ozier, Rosemary Cashman

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsIntervention (counseling)MedicineQuality of life (healthcare)Peer supportVolunteerSocial supportAdverse effectFamily medicinePhysical therapyPsychologyPsychiatryNursingInternal medicinePsychotherapist

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this pilot study was to investigate the impact of an intervention designed to enhance quality of life in newly diagnosed primary brain tumour (PBT) patients. The intervention involved a structured, one time meeting between newly diagnosed PBT patients and trained volunteer "veteran" PBT patients. METHODS: Two volunteers met for a single, one-on-one meeting with a total of 10 newly diagnosed PBT patients. A combination of questionnaires and interviews were used to investigate the impact of the intervention for both the new patients and the volunteers. RESULTS: The intervention appeared to be of substantial value for both groups of participants. Analysis revealed that the newly diagnosed patients experienced a range of benefits, including those related to the themes of: increased hope, valued guidance, hearing what it's really like, overcoming aloneness, and receiving a wake up call to what matters. Only relatively minor adverse effects and challenges were reported. CONCLUSIONS: The findings provide initial evidence that the developed intervention has the potential to be a safe, useful means of enhancing psycho-social well-being in newly diagnosed PBT patients.Further investigation into the potential of one-to-one, peer support for brain tumour patients is an important research priority.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.033
GPT teacher head0.355
Teacher spread0.322 · 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 designOther design
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

Citations20
Published2016
Admission routes2
Has abstractyes

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