@article{10.11648/j.ajpn.20261403.13,
  author = {Rosemary Judith Akoth Odhiambo},
  title = {Neurocognitive and Psychosocial Drivers of Digital 
Help-seeking Among Kenyan Youth: Self-diagnosis, Affect Regulation, and Algorithm-shaped Symptom Attribution},
  journal = {American Journal of Psychiatry and Neuroscience},
  volume = {14},
  number = {3},
  pages = {69-82},
  doi = {10.11648/j.ajpn.20261403.13},
  url = {https://doi.org/10.11648/j.ajpn.20261403.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajpn.20261403.13},
  abstract = {Digital platforms have become primary spaces through which Kenyan youth interpret distress, search for mental health information, and seek coping support. Yet little Kenya-specific research explains why young people increasingly engage in self-diagnosis and symptom attribution through online environments, or how neurocognitive processes interact with psychosocial drivers and algorithmic exposure to shape help-seeking trajectories. This paper examines the neurocognitive and psychosocial mechanisms underlying digital help-seeking among Kenyan youth, focusing on three linked domains: (i) self-diagnosis as a sense-making strategy, (ii) affect regulation motives that reinforce online searching and scrolling, and (iii) algorithm-shaped symptom attribution, whereby repeated exposure to mental health narratives influences symptom labeling and perceived identity. Using an exploratory design combining digital landscape synthesis, theory-guided review, and stakeholder-informed interpretation, we develop a mechanistic framework explaining how uncertainty reduction, attentional capture, reinforcement learning, social proof, and parasocial trust interact with stigma, service constraints, and peer norms to produce distinctive digital help-seeking patterns. We argue that self-diagnosis is often psychologically adaptive in the short term because it provides emotional relief, coherence, and belonging; however, it can also generate maladaptive cycles through diagnostic anchoring, confirmation bias, and algorithmically amplified symptom salience. The paper proposes an integrated model of digital symptom attribution and identifies governance priorities including credibility cues, algorithm-aware psychoeducation, and referral integration that links high-risk digital trajectories to professional support. The framework offers a foundation for Kenya-specific research, including survey-based measurement of cognitive mechanisms, platform exposure mapping, and longitudinal designs to evaluate whether algorithm-driven symptom narratives intensify distress or improve help-seeking outcomes.},
 year = {2026}
}

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TY  - JOUR
T1  - Neurocognitive and Psychosocial Drivers of Digital 
Help-seeking Among Kenyan Youth: Self-diagnosis, Affect Regulation, and Algorithm-shaped Symptom Attribution
AU  - Rosemary Judith Akoth Odhiambo
Y1  - 2026/08/06
PY  - 2026
N1  - https://doi.org/10.11648/j.ajpn.20261403.13
DO  - 10.11648/j.ajpn.20261403.13
T2  - American Journal of Psychiatry and Neuroscience
JF  - American Journal of Psychiatry and Neuroscience
JO  - American Journal of Psychiatry and Neuroscience
SP  - 69
EP  - 82
PB  - Science Publishing Group
SN  - 2330-426X
UR  - https://doi.org/10.11648/j.ajpn.20261403.13
AB  - Digital platforms have become primary spaces through which Kenyan youth interpret distress, search for mental health information, and seek coping support. Yet little Kenya-specific research explains why young people increasingly engage in self-diagnosis and symptom attribution through online environments, or how neurocognitive processes interact with psychosocial drivers and algorithmic exposure to shape help-seeking trajectories. This paper examines the neurocognitive and psychosocial mechanisms underlying digital help-seeking among Kenyan youth, focusing on three linked domains: (i) self-diagnosis as a sense-making strategy, (ii) affect regulation motives that reinforce online searching and scrolling, and (iii) algorithm-shaped symptom attribution, whereby repeated exposure to mental health narratives influences symptom labeling and perceived identity. Using an exploratory design combining digital landscape synthesis, theory-guided review, and stakeholder-informed interpretation, we develop a mechanistic framework explaining how uncertainty reduction, attentional capture, reinforcement learning, social proof, and parasocial trust interact with stigma, service constraints, and peer norms to produce distinctive digital help-seeking patterns. We argue that self-diagnosis is often psychologically adaptive in the short term because it provides emotional relief, coherence, and belonging; however, it can also generate maladaptive cycles through diagnostic anchoring, confirmation bias, and algorithmically amplified symptom salience. The paper proposes an integrated model of digital symptom attribution and identifies governance priorities including credibility cues, algorithm-aware psychoeducation, and referral integration that links high-risk digital trajectories to professional support. The framework offers a foundation for Kenya-specific research, including survey-based measurement of cognitive mechanisms, platform exposure mapping, and longitudinal designs to evaluate whether algorithm-driven symptom narratives intensify distress or improve help-seeking outcomes.
VL  - 14
IS  - 3
ER  - 

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