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Record W4234818041 · doi:10.2196/resprot.8322

Correction: Development of a Web-Based Intervention for Addressing Distress in Caregivers of Patients Receiving Stem Cell Transplants: Formative Evaluation With Qualitative Interviews and Focus Groups

2017· erratum· en· W4234818041 on OpenAlexvenueno aff
Nicole Pensak, Tanisha Joshi, Teresa L. Simoneau, Kristin Kilbourn, Alaina L. Carr, Jean S. Kutner, Mark L. Laudenslager

Bibliographic record

VenueJMIR Research Protocols · 2017
Typeerratum
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
Fundersnot available
KeywordsFormative assessmentFocus groupIntervention (counseling)Qualitative researchMedical educationDistressPsychologyMedicineNursingClinical psychologyPedagogySociology

Abstract

fetched live from OpenAlex

Background: Caregivers of cancer patients experience significant burden and distress including depression and anxiety. We previously demonstrated the efficacy of an eight session, in-person, one-on-one stress management intervention to reduce distress in caregivers of patients receiving allogeneic hematopoietic stem cell transplants (allo-HSCT). Objective: The objective of this study was to adapt and enhance the in-person caregiver stress management intervention to a mobilized website (eg, tablet, smartphone, or computer-based) for self-delivery in order to enhance dissemination to caregiver populations most in need. Methods: We used an established approach for development of a mhealth intervention, completing the first two research and evaluation steps: Step One: Formative Research (eg, expert and stakeholder review from patients, caregivers, and palliative care experts) and Step Two: Pretesting (eg, Focus Groups and Individual Interviews with caregivers of patients with autologous HSCT (auto-HSCT). Step one included feedback elicited for a mock-up version of Pep-Pal session one from caregiver, patients and clinician stakeholders from a multidisciplinary palliative care team (N=9 caregivers and patient stakeholders and N=20 palliative care experts). Step two included two focus groups (N=6 caregivers) and individual interviews (N=9 caregivers) regarding Pep-Pal’s look and feel, content, acceptability, and potential usability/feasibility. Focus groups and individual interviews were audio-recorded. In addition, individual interviews were transcribed, and applied thematic analysis was conducted in order to gain an in-depth understanding to inform the development and refinement of the mobilized caregiver stress management intervention, Pep-Pal (PsychoEducation and skills for Patient caregivers). Results: Overall, results were favorable. Pep-Pal was deemed acceptable for caregivers of patients receiving an auto-HSCT. The refined Pep-Pal program consisted of 9 sessions (Introduction to Stress, Stress and the Mind Body Connection, How Thoughts Can Lead to Stress, Coping with Stress, Strategies for Maintaining Energy and Stamina, Coping with Uncertainty, Managing Changing Relationships and Communicating Needs, Getting the Support You Need, and Improving Intimacy) delivered via video instruction through a mobilized website. Conclusions: Feedback from stakeholder groups, focus groups, and individual interviews provided valuable feedback in key areas that was integrated into the development of Pep-Pal with the goal of enhancing dissemination, engagement, acceptability, and usability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.228
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.228
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.004
Scholarly communication0.0030.003
Open science0.0040.004
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0260.011

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 head0.272
GPT teacher head0.532
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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