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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)

This paper has been accepted and is currently in production.

It will appear shortly on 10.2196/mental.9946

The final accepted version (not copyedited yet) is in this tab.

The final, peer-reviewed published version of this preprint can be found here:

Predicting Caller Type From a Mental Health and Well-Being Helpline: Analysis of Call Log Data

Grigorash A, O'Neill S, Bond R, Ramsey C, Armour C, Mulvenna MD

Predicting Caller Type From a Mental Health and Well-Being Helpline: Analysis of Call Log Data

JMIR Ment Health 2018;5(2):e47

DOI: 10.2196/mental.9946

PMID: 29891472

PMCID: 6018228

Predicting Caller Type From a Mental Health and Well-Being Helpline: Analysis of Call Log Data

  • Alexander Grigorash; 
  • Siobhan O'Neill; 
  • Raymond Bond; 
  • Colette Ramsey; 
  • Cherie Armour; 
  • Maurice D Mulvenna

ABSTRACT

Background:

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.

Objective:

The objective of our study was to examine whether rule sets generated from decision tree classification, trained using features derived from callers’ several initial calls, could be used to predict what caller type they would become.

Methods:

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.

Results:

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 callers’ behavior exhibited during initial calls and their behavior over the lifetime of using the service.

Conclusions:

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.


 Citation

Please cite as:

Grigorash A, O'Neill S, Bond R, Ramsey C, Armour C, Mulvenna MD

Predicting Caller Type From a Mental Health and Well-Being Helpline: Analysis of Call Log Data

JMIR Mental Health. (forthcoming/in press)

DOI: 10.2196/mental.9946

URL: https://preprints.jmir.org/preprint/9946

PMID: 29891472

PMCID: 6018228

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.