Original research · 2026-07 edition

AI SEO Statistics: Faith Based Rehab Center (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

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The question bank

The questions we tested — sampled from real buyer journeys in faith based rehab center.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

Is a faith-based rehab program more effective for long-term sobriety than a standard clinical one?
What should I ask a religious recovery center about their medical detox protocols to make sure they are safe?
Can I find a Christian treatment center that also offers dual-diagnosis support for bipolar disorder?
How much does a private faith-based residential program typically cost per month without insurance?
Are there non-denominational spiritual rehabs that don't force a specific doctrine on patients?
What are the red flags to watch out for when looking at a church-run addiction center's success claims?
Does my Blue Cross Blue Shield plan usually cover inpatient stays at religious-affiliated recovery houses?
My daughter needs help immediately but wants a program that incorporates prayer; how do I vet a place quickly?
Show all 15 questions
How do I know if a faith-based rehab is licensed by the state or just operating as a ministry?
What is the daily schedule like in a Bible-based rehab compared to a secular 12-step facility?
Are there scholarships or grants available for people who can't afford a private Christian drug rehab?
Is it better to go to a faith-based rehab far away from home to avoid triggers or stay local for church support?
Do religious rehabs allow for Medication-Assisted Treatment like Suboxone or do they require total abstinence?
What qualifications should the counselors have at a reputable faith-based treatment facility?
Can a person who isn't religious still benefit from a faith-based recovery program if it's the only one available?

Model by model

27-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about faith based rehab center buyers.

Behavior rates across 15 faith based rehab center buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional47%13%33%40%
Suggests DIY first20%20%0%73%
Names specific providers20%33%33%60%
Gives price or cost info7%13%13%93%
Tells to check reviews13%7%0%80%
Tells to verify credentials60%67%47%47%
Mentions case studies / portfolio13%0%0%87%
Mentions local proximity40%20%13%40%
Gives selection criteria60%73%53%33%
Warns about red flags27%40%27%53%
Asks a clarifying question53%60%0%13%
Recommends multiple quotes0%7%0%93%

By model

How each assistant handled Faith Based Rehab Center questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same faith based rehab center questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 46.7% (ChatGPT) down to 13.3% (Claude), a 33-point gap on an identical question set.

Across the 15 faith based rehab center answers it produced, ChatGPT recommended hiring a professional in 46.7% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 20% of answers (about 0.5 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 60%, averaging 540 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 40%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 faith based rehab center answers it produced, Claude recommended hiring a professional in 13.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 33.3% of answers (about 1.1 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 40%, and told the buyer to verify credentials in 66.7%, averaging 289 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 73.3% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 faith based rehab center answers it produced, Gemini recommended hiring a professional in 33.3% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 33.3% of answers (about 0.8 distinct providers per answer) and included price or cost information 13.3% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 46.7%, averaging 235 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a faith based rehab center buyer to a professional (46.7%) and Claude the least (13.3%). ChatGPT produced the longest answers, at 540 words on average. Specific providers were named most often by Claude (33.3%) — even there, roughly one answer in 3 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 27 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a faith based rehab center buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 60% (Claude) — a 60-point spread.
  • Recommends hiring a professional: from 13.3% (Claude) to 46.7% (ChatGPT) — a 33-point spread.
  • Mentions local proximity: from 13.3% (Gemini) to 40% (ChatGPT) — a 27-point spread.
  • Suggests a DIY approach first: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.
  • Tells the buyer to verify credentials: from 46.7% (Gemini) to 66.7% (Claude) — a 20-point spread.

The widest single gap — asks a clarifying question, 60 points — means a faith based rehab center buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the faith based rehab center market.

Where they agree

The points of near-consensus in Faith Based Rehab Center.

On other behaviors the three models move almost in lockstep — the points of near-consensus for faith based rehab center, where all three landed within a few points of each other:

  • Gives price or cost information: 6.7%–13.3% across all three (a 7-point spread).
  • Recommends multiple quotes: 0%–6.7% across all three (a 7-point spread).
  • Names a specific provider: 20%–33.3% across all three (a 13-point spread).
  • Tells the buyer to check reviews: 0%–13.3% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "gives price or cost information" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (13.3%).

Every behavior, measured

All twelve coded behaviors for Faith Based Rehab Center, averaged across the three models.

The behaviors AI models reproduce most often for faith based rehab center are gives selection criteria (62.2% on average), tells the buyer to verify credentials (57.8%) and asks a clarifying question (37.8%); the rarest are recommends multiple quotes (2.2%), mentions case studies or portfolio (4.4%) and tells the buyer to check reviews (6.7%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Gives selection criteria: 62.2% on average (ChatGPT 60%, Claude 73.3%, Gemini 53.3%) — a 20-point spread.
  • Tells the buyer to verify credentials: 57.8% on average (ChatGPT 60%, Claude 66.7%, Gemini 46.7%) — a 20-point spread.
  • Asks a clarifying question: 37.8% on average (ChatGPT 53.3%, Claude 60%, Gemini 0%) — a 60-point spread.
  • Recommends hiring a professional: 31.1% on average (ChatGPT 46.7%, Claude 13.3%, Gemini 33.3%) — a 33-point spread.
  • Warns about red flags or scams: 31.1% on average (ChatGPT 26.7%, Claude 40%, Gemini 26.7%) — a 13-point spread.
  • Names a specific provider: 28.9% on average (ChatGPT 20%, Claude 33.3%, Gemini 33.3%) — a 13-point spread.
  • Mentions local proximity: 24.4% on average (ChatGPT 40%, Claude 20%, Gemini 13.3%) — a 27-point spread.
  • Suggests a DIY approach first: 13.3% on average (ChatGPT 20%, Claude 20%, Gemini 0%) — a 20-point spread.
  • Gives price or cost information: 11.1% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 13.3%) — a 7-point spread.
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 4.4% on average (ChatGPT 13.3%, Claude 0%, Gemini 0%) — a 13-point spread.
  • Recommends multiple quotes: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the faith based rehab center buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the faith based rehab center buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 57.8%. Warning about red flags or scams appeared in 31.1%.

On structuring the decision, a selection-criteria checklist showed up in 62.2% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for faith based rehab center is "recommends multiple quotes" at 2.2% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name Faith Based Rehab Center providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 faith based rehab center answers, a specific provider was named in 28.9% of responses on average — roughly 0.8 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for faith based rehab center: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Faith Based Rehab Center questions cover.

The 15 questions behind every percentage on this page were drawn from real faith based rehab center (healthcare services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact faith based rehab center question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.

How to read this

A note on the numbers.

A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific faith based rehab center question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →