Currently accepted at: JMIR Mental Health
Date Submitted: Jan 25, 2018
Open Peer Review Period: Jan 26, 2018 - May 2, 2018
Date Accepted: May 2, 2018
(closed for review but you can still tweet)
Predicting Caller Type From a Mental Health and Well-Being Helpline: Analysis of Call Log Data
This paper presents an analysis of call data records pertaining to a telephone helpline in Ireland among individuals seeking mental health and well-being support and among those who are in a suicidal crisis.
The objective of our study was to examine whether rule sets generated from decision tree classification, trained using features derived from callersâ€™ several initial calls, could be used to predict what caller type they would become.
Machine learning techniques were applied to the call log data, and five distinct patterns of caller behaviors were revealed, each impacting the helpline capacity in different ways.
The primary findings of this study indicate that a significant model (P<.001) for predicting caller type from call log data obtained from the first 8 calls is possible. This indicates an association between callersâ€™ behavior exhibited during initial calls and their behavior over the lifetime of using the service.
These data-driven findings contribute to advanced workload forecasting for operational management of the telephone-based helpline and inform the literature on helpline caller behavior in general.
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