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Record W4416634010 · doi:10.2196/76266

Health Care Providers’ Perceptions of Unmet Needs Among African American Cancer Caregivers: Qualitative Investigation Among US Medical Professionals

2025· article· en· W4416634010 on OpenAlexvenueno aff
Brad Love, Gerold Dermid, Sean Upshaw, Amy Stark

Bibliographic record

VenueJMIR Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsAfrican americanRestructuringPerceptionCultural humilityHealth careHumilityQualitative researchHealth professionalsHealth equity

Abstract

fetched live from OpenAlex

BACKGROUND: African American caregivers are more likely to be sole unpaid caregivers, spend more hours on caregiving tasks, and receive less external support compared to White caregivers; yet, limited research focuses on their specific needs. Even less attention has been paid to health care provider perspectives on how to better support this population, despite providers' critical role in connecting caregivers to resources and implementing systems-level changes. OBJECTIVE: This study aimed to understand health care providers' experiences supporting African American cancer caregivers and to identify actionable recommendations for improving care. Specific objectives were to (1) identify unmet needs that providers observe among African American cancer caregivers, (2) explore barriers preventing these needs from being met, and (3) elicit provider recommendations for interventions to enhance caregiver support. METHODS: Between January and May 2023, we conducted semistructured online interviews with 12 health care providers across 7 US states. Providers were purposively sampled from facilities serving patient populations with ≥20% African American representation. Participants included physicians (n=7), social workers (n=2), nurses (n=2), and other providers (n=1), with 58% identifying as Black or African American and 83% having more than 15 years of clinical experience. Interviews lasted ~60 minutes and were conducted via Zoom (Zoom Video Communications, Inc) with audio recording. Data were analyzed using condensed thematic analysis guided by the McKillip needs assessment framework and socioecological model. RESULTS: Thematic analysis revealed 2 overarching categories of findings. First, providers identified three types of unmet needs among African American cancer caregivers (1) practical needs, including transportation, financial constraints, and competing family obligations; (2) social-emotional needs, including stress, burnout, and fear; and (3) cultural barriers, including medical mistrust rooted in historical trauma, "superhero Black woman" expectations, tensions between faith and medical treatment, and stigma around mental health. Second, providers offered four themes of recommendations for transformational change: (1) formal acknowledgment and compensation of caregiving as essential work; (2) integration of caregivers as equal members of multidisciplinary care teams; (3) recognition and leveraging of cultural assets, including strong family networks, community values, and faith-based support; and (4) strengthening providers' roles as hubs for individual-level support and systems-level advocacy. CONCLUSIONS: Health care providers readily identify substantial unmet needs among African American cancer caregivers and offer practice-based recommendations that extend beyond individual-level support to emphasize structural and systems transformation. Findings suggest that meaningful improvement requires multilevel intervention. This includes policy changes to formalize and compensate caregiving work, organizational restructuring to integrate caregivers into care teams, provider training in cultural humility and asset-based approaches, and institutional commitment to addressing historical trauma and rebuilding trust with African American communities. This novel provider-focused approach offers actionable pathways for clinical settings to reduce disparities and improve outcomes for African American cancer caregivers and the patients they support.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.405
Teacher spread0.386 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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