Predicting Demographic Contraction as an Upstream Signal for Health-System Planning: A Persistence-Corrected Machine-Learning Framework.

Kim DH, Na HJ

DOI: 10.3390/healthcare14203494
Published: 2026 Oct 17
Container: Healthcare (Basel, Switzerland)
Publisher: Not recorded
Open access: yes

Credibility signals

limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
cautionDOI resolves: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility. Source: DOAJ; license: CC0
not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredROR affiliation: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

Cite this work

BibTeX

@article{allodium:10.3390/healthcare14203494,
  title = {Predicting Demographic Contraction as an Upstream Signal for Health-System Planning: A Persistence-Corrected Machine-Learning Framework.},
  author = {Kim DH and Na HJ},
  year = {2026},
  journal = {Healthcare (Basel, Switzerland)},
  doi = {10.3390/healthcare14203494},
  url = {https://doi.org/10.3390/healthcare14203494}
}

RIS

TY  - JOUR
TI  - Predicting Demographic Contraction as an Upstream Signal for Health-System Planning: A Persistence-Corrected Machine-Learning Framework.
AU  - Kim DH
AU  - Na HJ
PY  - 2026
JO  - Healthcare (Basel, Switzerland)
DO  - 10.3390/healthcare14203494
UR  - https://doi.org/10.3390/healthcare14203494
ER  -

APA

DH, K., & HJ, N. (2026). Predicting Demographic Contraction as an Upstream Signal for Health-System Planning: A Persistence-Corrected Machine-Learning Framework.. Healthcare (Basel, Switzerland). https://doi.org/10.3390/healthcare14203494

Source records

pubmed · retrieved 2026-11-10T19:12:40.790Z