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Record W4389082976 · doi:10.11124/jbies-23-00044

Psychosocial interventions that target adult cancer survivors’ reintegration into daily life after active cancer treatment: a scoping review

2023· review· en· W4389082976 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJBI Evidence Synthesis · 2023
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsNova Scotia Health AuthorityDalhousie University
FundersCanadian Institutes of Health ResearchDalhousie University
KeywordsPsychosocialPsychological interventionCINAHLSurvivorship curvePsycINFOMedicineCancer survivorMEDLINECancerSocial supportGerontologyQuality of life (healthcare)PsychologyPsychiatryPsychotherapistNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: This review explored psychosocial interventions targeting adult cancer survivors' reintegration following active cancer treatment. This included the types of interventions tested and the tools used to measure reintegration. INTRODUCTION: Cancer survivors face lingering health issues following the completion of cancer treatment. Many cancer survivors still experience unmet psychosocial care needs despite receiving follow-up care. Further, many survivorship interventions do not specifically address outcomes important to survivors. A number of primary studies have identified reintegration as an outcome important to cancer survivors. Reintegration is a concept that focuses on returning to normal activities, routines, and social roles after cancer treatment; however, it is emerging and abstract. INCLUSION CRITERIA: Studies involving adult cancer survivors (18 years or older at diagnosis) of any cancer type or stage were included in this review. Studies with psychosocial interventions targeted at reintegrating the person into daily life after cancer treatment were included. Interventions addressing clinical depression or anxiety, and interventions treating solely physical needs that were largely medically focused were excluded. METHODS: A literature search was conducted in MEDLINE (Ovid), CINAHL (EBSCOhost), and Embase. Gray literature was searched using ProQuest Dissertations and Theses (ProQuest). Reference lists of included studies were searched. Studies were screened at the title/abstract and full-text levels, and 2 independent reviewers extracted data. Manuscripts in languages other than English were excluded due to feasibility (eg, cost, time of translations). Findings were summarized narratively and reported in tabular and diagrammatic format. RESULTS: The 3-step search strategy yielded 5617 citations. After duplicates were removed, the remaining 4378 citations were screened at the title and abstract level, then the remaining 306 citations were evaluated at the full-text level by 2 independent reviewers. Forty studies were included that evaluated psychosocial interventions among adult cancer survivors trying to reintegrate after active cancer treatment (qualitative n=23, mixed methods n=8, quantitative n=8, systematic review n=1). Included articles spanned 10 different countries/regions. Over half of all included articles (n=25) focused primarily on breast cancer survivors. Many studies (n=17) were conducted in primary care or community-based settings. The most common types of interventions were peer-support groups (n=14), follow-up education and support (n=14), exercise programs (n=6), and multidisciplinary/multicomponent programs (n=6). While the majority of included studies characterized the outcome qualitatively, 9 quantitative tools were also employed. CONCLUSIONS: This review identified 6 types of interventions to reintegrate survivors back into their daily lives following cancer treatment. An important thread across intervention types was a focus on personalization in the form of problem/goal identification. Given the number of qualitative studies, future research could include a qualitative systematic review and meta-aggregation. Quantitative tools may not be as effective for evaluating reintegration. More primary studies, including mixed methods studies, utilizing consistent measurement tools are required. Furthermore, this work provides a basis for future research to continue examining the complexity of implementing such interventions to successfully achieve reintegration. To do so, primary studies evaluating interventions from an implementation science and complex systems perspective would be useful. REVIEW REGISTRATION: Open Science Framework https://osf.io/r6bmx.

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.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.677
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.099
GPT teacher head0.442
Teacher spread0.344 · 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